Subvolume identification for prediction of treatment outcome

ABSTRACT

Physiological imaging-defined subvolumes of tissues/disease are identified to yield spatially-defined prognostic and/or predictive indicators and/or focal therapy targets within such tissues, in particular tumors, for evaluation over time, for example, prior to and after a therapy treatment. Medical image data is analyzed to delineate subvolumes of tissue based upon multiple physiological, metabolic, and biological imaging properties, where those subvolumes are extracted and analyzed in a probabilistic manner to associate with one or more abnormal or disease phenotype conditions.

RELATED APPLICATIONS

This application claims benefit to U.S. Patent Application No.61/656,323, filed on Jun. 6, 2012, and entitled “SUBVOLUMEIDENTIFICATION FOR PREDICTION OF TREATMENT OUTCOME,” the entirety ofwhich is hereby incorporated herein by reference for all purposes.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under Contract Nos. RO1NS064973, R21 CA113699, and 3P01 CA59827 awarded by the NationalInstitutes of Health. The government has certain rights in theinvention.

FIELD OF THE INVENTION

The present invention relates generally to a method to identifyphysiological, metabolic, molecular, and/or biological imaging-definedabnormalities (phenotypes) of diseases for diagnosis, prognosis orprediction of therapy response and/or for intensified local treatment.

BACKGROUND OF THE RELATED ART

When treating tumors, the effectiveness of a treatment is determined byanalyzing tumor size, comparing the size pre-treatment andpost-treatment. Typically, tumor size is measured through an analysis ofmagnetic resonance imaging (MRI) or other image data. Such techniqueshave been used for years and are relatively effective. However,unfortunately, a tumor size change often occurs late, and therefore thepatient loses the time window for optimal treatment.

In recent years, physiological, metabolic and molecular imaging has beendeveloped and tested for early prediction of tumor treatment responseand outcome with a promise that a biological change in the tumor couldoccur prior to its size change. Analysis of physiological, metabolic andmolecular imaging in the tumor is often done by averaging the values ofthe relevant physiological imaging parameter in the tumor and thencomparing the mean values between pre and post therapy. This is a simplebut not effective approach due to neglecting the heterogeneousdistribution of the physiological parameter in the tumor, particularlyin large tumors. An average value of a physiological imaging parameterin the tumor can wash out the sensitivity of the parameter to a changein the tumor. Indeed, this sensitivity problem is a direct result of theinability of existing techniques to effectively discriminate theinformation within a tumor image. Tumor image data may reflect numerousdifferent phenotypes/conditions within a tissue mass, but without anability to properly discriminate between these differentphenotypes/conditions, from a medical image data, effective analysis anddiagnosis is limited.

A voxel-by-voxel analysis of a change in the physiological, metabolic ormolecular imaging parameter of the tumor pre and post therapy is animprovement but requires an accurate registration of a pair of imagesobtained pre and post therapy. When there is tumor growth and shrinkagefrom pre to post therapy, the results of the voxel-by-voxel analysis ofthe physiological imaging are often incorrect or, at a minimum,misleading. The reasons for this are numerous, but the primary culpritis mis-registration of image data at the voxel level. As a tumor changesin size, it is difficult to register with satisfactory certainty a firstimage of a tumor, taken at one point, e.g., pre-treatment, with thesecond image of that same tumor, now changed, taken at a second point,e.g., post treatment. This image registration problem commonly requirescomplex image analysis to correct and still limits the effectiveness ofpractitioners to use physiological, metabolic and molecular imaging forproper tumor treatment assessment.

To derive a physiological imaging parameter from dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) data or other dynamicPET/SPECT data involves time-consuming pharmacokinetic modeling. Tryingto diagnose physiological conditions from that image parameter is proneto error, poor reproducibility and lack of accuracy. For example, inanalyzing a physiological imaging parameter of a tumor for therapyresponse, a parameter such as Gd-DTPA (gadopentetic acid) transferconstant (K^(trans)) derived from DCE-MRI by a pharmacokinetic model issensitive to noise in the DCE-MRI, and has a reproducibility ofapproximately 20%, which reduces the minimum change that can be detectedpost therapy compared to pre therapy. Ultimately, medical imaging isused in tumor treatment to determine which treatments are more effectiveand where to direct those treatments within the patient. For example,intensity-modulated radiotherapy (IMRT) seeks to deliver high-precisionnonuniform dose patterns through ‘painting’ and ‘sculpting’ doses in aradiation target volume in order to improve the therapeutic ratio andtreatment outcome. The conventional IMRT attempts to optimize anddeliver a treatment plan having a uniform dose distribution within atarget volume delineated primarily based upon anatomic images ofcomputed tomography (CT) and/or MRI. Geometrically conforming high doseswithin the target volume by IMRT can reduce dose-spread into normaltissue and organs at risk. However, target volume delineation based uponanatomic information provided by CT images and MRI images is limited.Also, considering spatially-heterogeneous biological properties of atumor, a uniform dose distribution within a target volume might not leadto an optimal treatment outcome.

In any event, voxel-by-voxel image analyses, while effective to anextent, are limiting. A more useful approach is desired.

SUMMARY OF THE INVENTION

The present techniques address the foregoing problems by providingtechniques for assessing imaging-defined abnormalities (phenotypes) ofdiseases in regions of interests, through physiological, metabolic,biologic, and/or molecular imaging, to identify abnormalityprobabilities of a tumor or other disease, and more specificallymultiple biological subvolumes that may be analyzed against any numberof disease conditions. In this way, not only are diagnosticdeterminations possible, but prognostic or predictive determinations canbe made for predicting a therapy response and outcome of treatment on anidentified subvolume. From here, the techniques may be used to provide abiological-imaging defined target for intensive treatments, e.g., formore effective ‘painting’ and ‘sculpting’ of radiation dosage treatmentsor any number of tumor treatment optimizations.

In some examples, the present techniques are built upon the developmentof a functional image analysis framework to integrate physiological,metabolic, molecular, and/or biological information from a variety offunctional imaging sources, to delineate imaging-defined “phenotype”subvolumes of a tumor and to relate them to treatment response andoutcome.

The techniques are designed to delineate subvolumes of a tumor basedupon its heterogeneous distributions of physiological, metabolic, and/orbiological imaging parameters. For example, the techniques may assigneach voxel a probabilistic membership function belonging to thephysiological parameter classes defined in a sample of tumors and thencalculates the related subvolumes in each tumor. In some examples, thepresent techniques were used to delineate tumor subvolumes from regionalcerebral blood volume (rCBV) and Gd-DTPA (gadopentetic acid) transferconstant (K^(trans)) from blood plasma to tissue in patients who havehad brain metastases and who received whole brain radiation therapy(WBRT). A subvolume analysis of the tumor, and in particular in changesto the subvolume after starting therapy, was used to assess and predicttreatment response from pre-treatment to post-RT. Changes in the rCBV(or en-defined subvolumes of the tumors after starting RT were evaluatedfor differentiating responsive, stable and non-responsive tumors using,from which performance metrics for predicting tumor response to WBRT wasdeveloped, with suitable results from Receiver Operating Characteristic(ROC) analysis. The ROC analysis showed that this new metric wassignificantly better than the decrease in the gross tumor volume (GTV)observed during the same time interval for predicting post-therapyresponse, suggesting that physiological imaging adds discriminatoryinformation compared to the volumetric change.

Other techniques herein include developing a diffusion abnormality index(DAI) to quantify the extent of abnormality of the tumor apparentdiffusion coefficient (ADC) histogram compared to normal tissue. Anormal tissue ADC histogram, H_(NT)(ADC), is obtained in a normal brainvolume of 3-4 cc with the peak normalized to 1. The tumor ADC histogramthat usually spreads beyond H_(NT) (ADC) is divided by H_(NT) (ADC) into3 categories: low (high cellularity), normal, and high (edema andnecrosis) ADC. An abnormal diffusion probability function (DAProF) ofthe tumor is then defined by 1−H_(NT) (ADC) and band-pass filtered toreduce noise influence at the two tails of the histogram. Given thatchanges in low and high ADCs could have different roles in responseassessment, a factor (0<α<1) is used to weight the low ADC contributionto the DAProF related to high ADC's. As a result, a DAI is defined as anintegral of the DAProF-weighted tumor ADC histogram. The performance ofDAI for predicting the post-treatment radiographic response was alsoevaluated by Receiver Operating Characteristic analysis and comparedwith changes in gross tumor volume (GTV) observed during the same timeinterval. The performance of DAI worsened for the radioresistant lesionstreated by WBRT combined with Bortezomib but still better than changesin the GTV, suggesting that the physiological change occurs prior to thevolumetric change.

In accordance with an embodiment, a method of analyzing medical imagedata of a region of interest in a sample tissue, the method comprises:obtaining, at a computer system, the medical image data of the region ofinterest, the medical image data containing image segments; identifying,at the computer system, one or more candidate physiological, metabolic,molecular and/or biologic parameters that may indicate an abnormal ordisease phenotype condition within the region of interest; analyzing, ina subvolume analysis engine, each of the image segments using analgorithm to determine, for each image segment, a probability functionfor each of the identified one or more candidate physiological,metabolic, and/or biologic parameters; and modeling the image segments,in the subvolume analysis engine, and analyzing the resulting model toidentify the abnormal or disease phenotype condition within the regionof interest, where the identification produces a diagnosticdetermination of the region of interest, a prognostic determination ofthe target tissue, or a predictive determination of the target tissue.

In accordance with another embodiment, an apparatus for analyzingmedical image data of a region of interest in a sample tissue, theapparatus comprises: a computer system having a processor executinginstructions that, when executed, (i) obtain the medical image data ofthe region of interest, the medical image data containing imagesegments, and (ii) identify one or more candidate physiological,metabolic, molecular and/or biologic parameters that may indicate anabnormal or disease phenotype condition within the region of interest;and the computer system further comprising a subvolume analysis engineto (i) analyze each of the image segments using an algorithm todetermine, for each image segment, a probability function for each ofthe identified one or more candidate physiological, metabolic, and/orbiologic parameters, (ii) model the image segments, in the subvolumeanalysis engine, and (iii) analyze the resulting model to identify theabnormal or disease phenotype condition within the region of interest,where the identification produces a diagnostic determination of theregion of interest, a prognostic determination of the target tissue, ora predictive determination of the target tissue.

The features, functions, and advantages can be achieved independently invarious embodiments of the present invention or may be combined in yetother embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a graph that illustrates an example of a Pre-RT rCBVhistogram of a typical lesion of brain metastases;

FIG. 1B is a depiction of a graph that illustrates an example of apooled probability density function of a Pre-RT rCBV from all lesionsand three probability membership functions determined by FCM clustering;

FIG. 2 is a depiction of a graph that illustrates examples of a Pre-RTT1 weighted image, rCBV, and k^(trans) maps of a patient with two brainmetastases taken at times relative to radiation therapy;

FIG. 3 is a depiction of a graph that illustrates examples of a typicalDCE curve of a voxel in a brain metastatic lesion and the first threeprincipal components;

FIG. 4A is a depiction of a graph that illustrates Receiver OperatingCharacteristic curves of the metrics in Table 2 for predictingresponsive tumors;

FIG. 4B is a depiction of a graph that illustrates area under the ROCcurves vs. a in Equation (7);

FIG. 4C is a depiction of a graph that illustrates Receiver OperatingCharacteristic curves of the metrics in Table 5 for predictingresponsive tumors;

FIG. 4D is a depiction of a graph that illustrates area under the ROCcurves vs. α in Equation (20);

FIG. 5 are images that illustrate examples of a post-contrastTI-weighted MRI, T2 FLAIR, blood volume, and blood flow slices of apatient pre-RT and at week 2;

FIG. 6 illustrates the process of classification of the whole grosstumor volume into subvolumes based upon their characteristicphysiological features;

FIG. 7A is an image of a representative example of the subvolumes of theprimary gross tumor volume with low blood volume for a local failurecase of head and neck cancer;

FIG. 7B is an image of a representative example of the subvolumes of theprimary gross tumor volume with low blood volume for a local controlcase of head and neck cancer;

FIG. 8 is a graph that illustrates correlation of subvolumes with lowblood volume at the pre-RT and during RT time-points;

FIG. 9 is a depiction of a graph that illustrates a comparison of fittedReceiver Operating Characteristic curves of five metrics for predictionof local failure of head and neck cancers;

FIG. 10 is a flow diagram of an example method for predicting treatmentvia subvolume identification;

FIG. 11A is a depiction of a graph that illustrates examples of apparentdiffusion coefficients histograms in a region of normal white matter, aprogressive brain metastasis prior to treatment and two 2 weeks afterstarting treatment;

FIG. 11B is a depiction of a graph that illustrates an example DiffusionAbnormality Probability Function of the same tumor as shown in FIG. 11A;

FIG. 12 are exemplary box plots that illustrate the change in differentcharacteristics for responsive, stable, and progressive lesions treatedby whole brain radiation therapy or in combination with Bortezomib as aradiation sensitizer;

FIG. 13A is a depiction of a graph that illustrates different examplesof Receiver Operating Characteristic curves for differentcharacteristics for predicting non-responsive tumors treated withradiation therapy alone;

FIG. 13B is a depiction of a graph that illustrates different examplesof Receiver Operating Characteristic curves for differentcharacteristics for predicting non-responsive tumors treated withradiation therapy in combination with Bortezomib as a radiationsensitizer;

FIG. 14 illustrates exemplary images of T1-weighted images, apparentdiffusion coefficient maps, and maps of diffusion abonormalityprobability functions at Pre-RT and at week 2 for a responsive lesionand for a progressive lesion;

FIG. 15 is a flow diagram of an example method of predicting treatmentof a tumor via diffusion abnormality index; and

FIG. 16 is a block diagram of an example system in which techniques forpredicting of treatment via subvolume identification are implemented.

DESCRIPTION OF DETAILED EXAMPLES

The present techniques provide image analysis of the heterogeneousdistribution of one or more candidate physiological, metabolic,molecular and/or biologic imaging parameters in the tumor to determinethe abnormality probability map and a quantitative abnormality metric.The abnormality metric and a change during the course of therapy aredesigned for diagnosis, prognosis and prediction of tumor response totreatment. When assessing a tumor response to treatment, this techniquedoes not require co-registration of images acquired at two differentpoints in time. Furthermore, the techniques do not use hard thresholdingto analyze the heterogeneous distribution of the physiological imagingparameters in the tumor. The techniques may be applied to physiological,metabolic, molecular and/or biologic imaging parameters (e.g., vascularparameters or features, cellular parameters or features, etc.) as wellas to the dynamic contrast agent/tracer uptake images directly, e.g.,DCE-MRI, dynamic PET/SPECT, with or without pharmacokinetic modeling.And the techniques can be used to analyze multiple physiological,metabolic, molecular and/or biologic parameters for testingdiscriminatory or redundant information, and to combine them into asingle metric.

The techniques herein differ from methods that generate parametricresponse maps (PRMs). In the PRM method, after co-registration of a pairof images acquired at two different time points over therapy, adifference of the image intensities before and after therapy at eachvoxel is analyzed; and a response value is assigned to each voxelaccording to its change above or below a cutoff threshold. Althoughanalyzing a voxel-wise change in a tumor is an interesting approach,mis-registration of the image voxels, particularly in the region where atumor shrinks or grows during the time interval, could compromise theresult. The techniques also differ from using a hard threshold tosegment the abnormal value of the heterogeneously-distributedphysiological imaging parameter. While using a threshold value tosegment a tumor volume is simple, the binary decision discards theparameter continuity at the threshold value. Furthermore, finding anadequate threshold value is always a challenge, and often doneempirically and sometimes arbitrarily.

The present techniques overcome these challenges, by using a smartalgorithm, e.g., fuzzy logic, a genetic algorithm, or Gaussian mixturemodels, to analyze physiological, metabolic, molecular, and/or biologicimaging-parameters or features of voxels from a sample of tumors. Thealgorithm may be globally-initiated in a training state from acollective sample and provide localized assessment of individual tumorsor regions in an analysis state. A probability density function orhistogram of the imaging-parameters or features from all the voxels ofthe sample of the tumors is created, and analyzed by a fuzzy logicalgorithm or other discrimination algorithm for determining theabnormality classifications and related probability functions. For a newtumor, after measuring and deriving the imaging parameters or features,the abnormality classifications determined from the sample of the tumorsare applied in order to assign each voxel of the new tumor a probabilityfunction of belonging to those image-defined abnormality classes. Then,an abnormality probability map of the tumor can be created as an outputfor visualization and/or a target for focal therapy, e.g., radiationboost target. Finally, a quantitative abnormality metric of the tumor iscreated according to its abnormality probability functions for diagnosisand prognosis of the tumor. A change in the abnormality metric duringthe course of therapy or post therapy, reflecting a change in thephysiological, metabolic, molecular or biologic imaging-parameters inthe tumor—note such changes often occur before a volumetric change isachieved for prediction of tumor response to therapy. As mentionedbefore, the present techniques do not depend upon voxel-wise accuracy ofregistration of a pair of images acquired pre and during or post therapyor use any hard threshold to determine the abnormality level of thetumor.

FIG. 10 illustrates an example implementation of the techniques herein,in particular, used to predict the response of a tumor to radiationtherapy. FIG. 10 illustrates an example technique that is based onsubvolume analysis of a tumor and identifies N (numbers) of voxel-basedphysiological imaging-derived parameters or features from a region ofinterest of a tissue sample that may contain tumors or other diseases.The system generates an n-dimensional probability density function (PDF)or histogram of the parameters or features from the samples. The systemthen creates an abnormality probability functions and models, such asclassifications (e.g., membership prototype vectors) from the n-D PDFusing fuzzy logic analysis (e.g., fuzzy c-means clustering), or otherdiscrimination analysis models. The abnormality probability functionsand classifications (e.g., the membership classifications) are appliedto the n-D PDF of the new tumor or disease to obtain abnormalityprobability functions of each voxel of the tumor or disease. Then thesystem creates an abnormality probability map of the tumor or disease asa visual output or target for focal therapy. The system may then, insome examples, create quantitative metrics from the abnormalityprobability function of the tumor or disease for determining adiagnosis, a prognosis or a prediction of treatment.

The techniques of FIG. 10 may be utilized on different properties of thesubvolumes of a tumor, such as the vascularity of subvolumes of thetumor, the cellularity of subvolumes of the tumor, etc. For example, themethod may utilize vascular properties, such as blood flow, bloodvolume, etc. in predicting treatment outcome. On the other hand, themethod may also examine cellularity properties (i.e., the water mobilityin tissue) to determine an abnormal diffusion coefficient in predictingtreatment outcome. The prediction results from the both properties maybe combined or added together to provide a more refined, accurateoverall prediction for treatment outcome.

FIG. 15 illustrates an example of another technique herein that alsoperforms an early assessment of a tumor response to radiation therapy.The example in FIG. 15 obtains diffusion weighted-images of tissue(e.g., tissue potentially containing a tumor or diseased tissue region)and generates apparent diffusion coefficients from the images. Thesystem then generates a histogram from the generated apparent diffusioncoefficients and also creates a diffusion abnormality probabilityfunction from the apparent diffusion coefficient histogram of normaltissue. The system further creates of diffusion abnormality probabilitymap of the identified tumor or diseased tissue region as a visual outputor target for focal therapy and generates a DAI from the apparentdiffusion coefficients of the tumor or disease for diagnosis, prognosisor prediction of treatment.

I. Subvolume Analysis of a Tumor

The technique of subvolume analysis of a tumor, as illustrated in FIG.10, is designed to delineate subvolumes of a tumor based upon itsheterogeneous distributions of physiological imaging-derived parametersor features. Furthermore, this subvolume analysis method may accept oneor more different imaging-derived parameters as inputs. For instance,one of type of physiological imaging-derived parameter may include oneor more parameters generated from a pharmacokinetics (PK) modelingtechnique. Using inputs from this PK modeling technique, the subvolumeanalysis technique assigns each image voxel a probabilistic membershipfunction belonging to the physiological parameter classes defined in asample of tumors, and then calculates the related subvolumes in eachtumor. Furthermore, this technique may define or model subvolumes ofvolume-weighted physiological parameters using probabilisticclassification of image voxels for features predictive of poor treatmentoutcome combined with clustering methods, for example.

The subvolume analysis technique of FIG. 10 may also use principalcomponent (PC) features derived from a principal component analysis(PCA) as inputs as opposed to using the PK modeling inputs. This PCA isa pharmacokinetic model free framework that analyzes the dynamiccontrast enhanced magnetic resonance imaging (DCE-MRI) data. This PCAtechnique further utilizes DCE-MRI data from image voxels of a sample ofbrain tumors to construct a DCE matrix. PCA may then be applied togenerate the PCs and subsequent projection coefficient maps. Next, amodeling technique, such as a pattern recognition based uponfuzzy-c-means clustering, may be used to delineate the tumor subvolumesrelating to the value of the projection coefficients. The relationshipbetween changes in different tumor subvolumes and treatment response maybe evaluated to differentiate responsive from stable and progressivetumors.

A. Pharmacokinetics Modeling in Subvolume Analysis

There is an understanding that high mean or rCBV or K^(trans) in thebrain tumor prior to therapy is correlated with a high tumor grade andworse outcome. A reduction in the high rCBV and/or K^(trans) in braintumors during radiation therapy is associated with better outcome. Allthese suggest that high-rCBV and/or K^(trans) in the brain tumor, as animaging-defined tumor “phenotype” and the related changes duringtherapy, are important prognostic and predictive indictors. Based uponthese observations in the brain tumors, the techniques herein wereevaluated to delineate the high rCBV (abnormality) probability map ofthe tumor and/or the abnormality subvolumes defined by rCBV, K^(trans),or combinations of the two parameters in the brain metastases. Asdiscussed above, it was found that the early change in the highrCBV-defined subvolume of the tumor is a better predictor for thepost-therapy tumor volumetric change, and has the potential to be usedfor selecting the lesion and defining the target for intensifiedtreatment. The results also indicated that K^(trans), although adifferent physiological parameter in the tumor compared to rCBV, doesnot add significant discriminatory information for assessment of brainmetastases response to WBRT. However, the techniques herein can be usedto test whether including other physiological imaging parameters inanalyses, e.g., apparent diffusion coefficient or 11 C-MET positronemission tomography (PET), fluorodeoxyglucose (FDG) uptake, etc. canimprove prediction for treatment response. This type of analysis canhelp determine whether multiple candidate physiological, metabolic,molecular, and/or biologic imaging parameters provide complementary orredundant information. Also, the analysis can be adapted to specifictumor types and treatment modalities.

Experiment 1

Patient Sample:

In an example implementation of the techniques described herein, twentypatients (11 women and 9 men, ages 41-76 years) diagnosed with brainmetastases were enrolled in a prospective MRI study approved by theinstitutional review board (Table 1). The histology included melanoma(11), non-small cell lung cancer (6), renal cell carcinoma (1), breastcancer (1), and head & neck squamous cell carcinoma (1). All patientsreceived WBRT with a total dose of 30 Gy in 10 fractions (13 patients)or 37.5 Gy in 15 fractions (7 patients). In addition, thirteen patientsreceived Bortezomib during WBRT as a radiation sensitizer. If a patienthad three brain metastatic lesions or less, all lesions were included inthis analysis. If a patient had more than three lesions, only the threelargest lesions were included. If a patient had more than three lesionslarger than 1 cm³, the lesions greater than 1 cm³ were also included. Asa total, 45 lesions with a median volume of 1.65 cm³ and a range of0.1-17.6 cm³ were analyzed.

TABLE 1 Patients Characteristics - Example 1 Total Concurrent Pt.Gender/Age No. of Volume range accumulated drug No. (Y) Histologylesions (cm³) dose/Fx (Gy) treatment 1 F/54 BC 3  4.23-11.78 37.5/2.5None 2 M/63 RCC 2 13.23-14.67 30/3 Bortezomib 3 M/41 M 3 0.150-1.2437.5/2.5 Bortezomib 4 F/60 NSCLC 1 0.518 37.5/2.5 None 5 F/52 M 1 2.7437.5/2.5 Bortezomib 6 F/45 M 1 2.07 30/3 Bortezomib 7 M/49 M 20.171-4.09 30/3 Bortezomib 8 F/51 NSCLC 3 0.503-4.55 30/3 Bortezomib 9M/61 M 4  6.64-17.67 37.5/2.5 Bortezomib 10 M/52 NSCLC 1 0.479 30/3 None11 F/55 M 2 0.421-0.545 30/3 Bortezomib 12 M/76 M 1 0.680 30/3Bortezomib 13 F/46 M 6  1.25-1.95 30/3 Bortezomib 14 F/57 M 2 0.941-1.5830/3 Bortezomib 15 F/64 NSCLC 1 0.108 37.5/2.5 None 16 M/60 M 30.179-1.31 30/3 Bortezomib 17 F/74 M 4 0.690-5.81 30/3 Bortezomib 18M/43 H&N SCC 1 0.601 30/3 None 19 M/58 NSCLC 3  2.38-10.69 30/3 None 20F/66 NSCLC 1 0.954 37.5/2.5 None Abbreviation: Pt. No. = patient number;Y = year; F = female; M = male; BC = breast cancer; RCC = renal cellcarcinoma; M = melanoma; NSCLC = non-small cell lung cancer; and H&N SCC= head and neck squamous cell carcinoma.

Imaging and Data Acquisition:

All patients had MRI scans on a Philips 3T scanner prior to radiationtherapy (Pre-RT, where RT is radiation therapy), 2 weeks after the startof RT (2 W), and 1 month after the completion of treatment (1M Post-RT).MRI scans included pre and post Gd-DTPA volumetric T1 weighted images,multi-slice 2D T2 weighted images, and volumetric dynamic contrastenhanced (DCE) T1 weighted images. The DCE-images were acquired in thesagittal plane with an image matrix of 128×128×80, a field-of-view of240×240×160 (mm), a voxel size of 2×2×2 (mm³), a flip angle (α) of 20°,and TE/TR of 1.04/5.14 msec.

Pre-Processing Image Analysis:

An image registration was used to align the DCE-MRI with the anatomicMRI, both of which were acquired at a same time point—therefore there isno tumor growth or shrinkage. The physician created a tumor volume onthe anatomic images, which can be transferred to the DCE-MRI that has alower spatial resolution. The general Toft model was used to fit DCE-MRIto obtain rCBV and K^(trans) maps with an arterial input function (AIF)determined from a region of interest (ROI) in the carotid artery. Eachlesion of interest was manually contoured by a physician on the post-GdT1 weighed images obtained pre-RT, 2 W and 1M post-RT. Then, the tumorcontours at each time point were transferred to the corresponding rCBVand K^(trans) maps.

Probability Density Functions of Physiological Parameters:

to analyze rCBV distributions in the lesions and subsequent changesduring treatment, a PDF of rCBV of a lesion was generated by using anon-parametric PDF estimator. The PDF includes 150 evenly-spaced pointsto cover the range of the rCBV values for all the lesions of interest. Avalue of the PDF at a point x, H(rCBV=x), of a lesion was calculated as:

H(rCBV=x)≡n _(i) :x−ε≦rCBV_(i) <x+ε  (1)

where n_(i) was the number of voxels within |rCBV_(i-x)|<ε, and ε was asmooth factor of H and set as

$ɛ = \frac{\sigma}{4}$

and where a denotes σ standard deviation of the rCBV distribution in thetumor. After calculating Pre-RT and 2 W PDFs for each lesion (H_(Pre)(x)and H_(2W)(x), respectively), H_(Pre)(x) was normalized to have an areaunder the PDF curve equal to one (∫H(x)=1). Then, the normalizedH_(Pre)(x)s of all the lesions were summed to generate a pooled PDF(H_(p)) of brain metastases, in which each lesion has an equalcontribution regardless of its size. An example of H(x) of a lesion isshown in FIG. 1A, and in particular, an example of the Pre-RT rCBVhistogram of a typical lesion with a tumor volume of 1.26 cm³. Note thatthe abnormal tails at both ends of H(x).

Probabilistic Membership Function:

previous studies have suggested that the rCBV distribution of a braintumor is abnormal compared to normal cerebral tissue. A renormalizationof tumor vasculature, such as decreasing the elevated rCBV andincreasing the low one, could be an indicator of a tumor response totreatment. The present techniques were used to determine a set ofprobability functions that are associated with high, intermediate andlow rCBV classes. Hence, H_(p)(rCBV) is partitioned into three classesusing fuzzy-c-means (FCM) clustering analysis by minimizing an objectivefunction J_(m):

J _(m)=Σ_(i=1) ^(N)Σ_(j=1) ^(C) P _(j)(rCBV_(i))^(m)∥rCBV_(i) −c _(j)∥²,123 m<∞  (2)

where c_(j) is a prototype vector of the j_(th) class, P_(j)(rCBV_(i))is a probabilistic membership of an rCBV value belonging to the jthclass, and m is a fuzzy exponent and chosen as 2. The solutions of Eq.(2) are determined iteratively by:

$\begin{matrix}{{c_{j} = \frac{\sum\limits_{i = 1}^{N}{{{P_{j}\left( {r\; {CBV}_{i}} \right)}^{m} \cdot r}\; {CBV}_{i}}}{\sum\limits_{i = 1}^{N}{P_{j}\left( {r\; {CBV}_{i}} \right)}^{m}}},} & (3) \\{{P_{j}\left( {r\; {CBV}_{i}} \right)} = {\frac{1}{\sum\limits_{k = 1}^{C}\left\lbrack {\frac{{r\; {CBV}_{i}} - c_{j}}{{r\; {CBV}_{i}} - c_{k}}} \right\rbrack^{\frac{2}{m - 1}}}.}} & (4)\end{matrix}$

until reaching stopping criteria. Note that the FCM cluster analysisdoes not classify an rCBV value into a single class (no hard threshold)rather generates a probabilistic function of an rCBV value that belongsto a class. The probabilistic membership function, P_(j)(rCBV), is a newrepresentation of an rCBV value of a tumor voxel (mathematicallytransfers the data from an rCBV space into a new space). An example, asshown in FIG. 1B, includes a pooled PDF (light gray) of the Pre-RT rCBVfrom all the lesions and the three probability membership functionsdetermined by FCM clustering. In this example, the pooled PDF ispartitioned into three classes: Representing low (dot-dashed),intermediate (dashed), and high (solid) rCBV classes. A similarcomputation is applied to K^(trans).

Physiological-Parameter Defined Tumor Subvolume:

a primary interest was to test if a change in a subvolume of the tumordefined by high, intermediate or low rCBV values is related to tumortreatment response. The system defines a subvolume (SV) of a tumor withlow, intermediate or high rCBV using the probabilistic membershipfunction P_(j)(rCBV) and calculated a percentage change in the SV fromPre-RT to two weeks (2 W) as follows:

$\begin{matrix}{{{\Delta \; {{SV}_{{{pre}\rightarrow{2W}},i}\left( {r\; {CBV}} \right)}} = {\frac{{{GTV}_{2W} \cdot {\int{{{P_{i}(x)} \cdot {H_{2W}(x)}}{x}}}} - {{GTV}_{Pre} \cdot {\int{{{P_{i}(x)} \cdot {H_{Pre}(x)}}{x}}}}}{{GTV}_{{Pre} - {RT}} \cdot {\int{{{P_{i}(x)} \cdot {H_{Pre}(x)}}{x}}}} \cdot 100}},\mspace{20mu} {i \in \left\{ {{low},{intermediate},{{and}\mspace{14mu} {high}}} \right\}}} & (5)\end{matrix}$

A similar calculation was applied to K^(trans).

A percentage change in the gross tumor volume (GTV) from pre-RT topost-RT was used as an endpoint for response assessment. Severalpatients did not have 3 or 6 months post treatment imaging follow-ups.For the patients in whom 3 and 6 months post-RT images were available,there were good correlations in the GTV changes between 1 and 3 monthspost RT and between 3 and 6 months post RT (data not shown). Also,previous studies indicated that brain metastases exhibit littlepseudo-response and pseudo-progression one month after RT. Therefore, apercentage change was used in the GTV from Pre-RT to 1 month post RT,ΔGTV_(Pre→1M Post-RT), as a measure of tumor response to therapy. FromPre-RT to 1M Post-RT, 16 tumors had a decrease in the GTV at least 25%,defined as responsive, 11 tumors had an increase at least 25%, definedas non-responsive, and the remaining 18 were defined as stable. It wasnoticed that there were heterogeneous responses of multiple lesions froma single patient. Thus, each lesion was considered independently.

Statistical Analysis:

first, as part of the technique, it was tested if there were anysignificant differences in the changes of {circumflex over(Δ)}SV_(Pre-RT→2W,i)(α_(i)) between responsive, stable, andnon-responsive tumors using Mann-Whitney U Test. To justify multiplecomparisons (e.g., 2, 3 or 6 parameters being simultaneous evaluated), ap-value<0.01 was considered as significant. Next, a Receiver OperatingCharacteristic (ROC) analysis was performed to evaluate sensitivity andspecificity of the significant metrics identified in the previous testfor predicting responsive tumors using software package ROCKIT althoughany ROC software analysis tool may be used, e.g., any of ROCFIT,LABROC4, PROPROC, CORROC, INDROC, ROCKIT and LABMRMC. Also, these newlydeveloped metrics were compared with the conventional ones, including apercentage change in the GTV from Pre-RT to 2 W, {circumflex over(Δ)}GTV_(Pre→2W), and a change in the mean rCBV values of a tumor frompre-RT to 2 W, {circumflex over (Δ)}μ_(Pre→2W)(rCBV), for predictingpost treatment response. The significant difference of the area underROC curves (AUC) between the metrics were compared by t-test, for whichthe standard error and the difference between the two AUCs werecalculated by the method proposed by DeLong et al.

Finally, it was tested if combining the two physiological parameters ofrCBV and K^(trans) could improve prediction for tumor response. To doso, first a joint histogram of the rCBV and K^(trans) of a lesion iscomputed, e.g., H(rCBV=x, K^(trans)=y). Then, a joint probabilityfunction, P_(i,j)(rCBV, K^(trans), α), is defined as follows:

$\begin{matrix}{{P_{i,j}\left( {{rCBV},K^{trans},\alpha} \right)} = \frac{{P_{i}({rCBV})} + {\alpha \cdot {P_{j}\left( K^{trans} \right)}}}{1 + \alpha}} & (6)\end{matrix}$

where α is a weighting factor of the two parameters and i and jε{low,intermediate, and high). Applying the joint probability function to Eq.(5), a percentage change in a subvolume of a tumor defined by rCBV andK^(trans) classes from Pre-RT to 2 W is given by:

$\begin{matrix}{{{\hat{\Delta}\; {{SV}_{{{Pre}\rightarrow{2W}},i,f}\left( {{rCBV},K^{trans},\alpha} \right)}} = {\frac{\begin{matrix}{{{GTV}_{2W} \cdot {\int{\int{{{P_{i,j}\left( {x,y,\alpha} \right)} \cdot {H_{2W}\left( {x,y} \right)}}{x}{y}}}}} -} \\{{GTV}_{Pre} \cdot {\int{\int{{{P_{i,j}\left( {x,y,\alpha} \right)} \cdot {H_{Pre}\left( {x,y} \right)}}{x}{y}}}}}\end{matrix}}{{GTV}_{Pre} \cdot {\int{\int{{{P_{i,j}\left( {x,y,\alpha} \right)} \cdot {H_{Pre}\left( {x,y} \right)}}{x}{y}}}}}~ \cdot 100}}\mspace{20mu} {i \in \left\{ {{low},{intermediate},{{and}\mspace{14mu} {high}}} \right\}}} & (7)\end{matrix}$

The weighting factor α was selected that led to a maximum area under theROC curve for predicting tumor response.

Probability Function Maps:

examples of maps of the abnormality probability functions belonging tothe class of high rCBV, high-K^(trans) and combinations of the two of aresponsive and a stable lesion at Pre-RT and at two weeks (2 W) areshown in FIG. 2. In particular, as shown in FIG. 2, the example mapsinclude a T1-weighted (top-left), rCBV (top middle) and Ktrans(top-right). The second row of FIG. 2 includes maps at pre-RT thatillustrate the probabilities of voxels belonging to classes with highrCBV (left), high K^(trans) (middle), and high for both rCBV andK^(trans) (right). The third row of FIG. 2 includes maps at end of RTthat illustrate the probabilities of voxels belonging to classes withhigh rCBV (left), high K^(trans) (middle), and high for both rCBV andK^(trans) (right). Note that the spatial distribution of the probabilityfunction map of the high rCBV class of a lesion can be different fromone of the high K^(trans) class, and both can change from Pre-RT to 2 W.For the responsive lesion, the voxel probability functions belonging tothe high rCBV-K^(trans) class were reduced to almost zero from Pre-RT to2 W, and for the stable lesion the reduction was in a much smallerextent.

Physiological-Parameter Defined Subvolumes:

it was found that the percentage decrease in the high-rCBV subvolumes ofthe tumors from Pre-RT to 2 W of the responsive group differedsignificantly from the non-responsive group (p<0.0072) and from a groupcombining non-responsive and stable tumors (p<0.0057), but marginallyfrom the stable group (p=0.033) (Table 2). Similar but much weakertrends were observed in the percentage decrease in the high-K transsubvolumes of the tumors between the groups. The percentage decrease inthe tumor subvolumes defined by both the high rCBV and high K^(trans)classes with an equal weighting from Pre-RT to 2 W differentiated thethree groups with improved statistical significances, compared to usingeither variable alone. Specifically, the responsive group significantlydiffered from the non-responsive group (p=0.0012) and from the groupcombining the non-responsive and stable tumors (p=0.0017). For theconventional metrics, the decrease in the mean tumor rCBV from Pre-RT to2 W of the responsive group differed significantly from the stable group(p<0.0049) and the group of combining the stable and non-responsivetumors (p<0.0066); and the percentage decrease in the GTVs from Pre-RTto 2 W of the responsive group differed significantly from thenon-responsive group (p<0.0039) but marginally from the group ofcombining the non-responsive and stable tumors (p<0.0124).

TABLE 2 Differences between Responsive, Stable, and Non-ResponsiveTumors Group of lesions R vs. S S vs. NR R vs. NR R vs. {S & NR} Metricp-value

SV_(pre→2W)

(rCBV) i = low 0.1086 0.2517 0.6392 0.1803 i = intermediate 0.27710.3339 0.0513 0.900 i = high 0.0338 0.3568 0.0072** 0.0057**

SV_(pre→2W)

(K^(trans)) j = low 0.1012 0.8750 0.1910 0.0773 j = intermediate 0.30880.2909 0.8243 0.5613 j = high 0.6863 0.0162* 0.0406* 0.4992

SV_(pre→2W,high,high)(rCBV, K^(trans), 1) 0.0218* 0.0758 0.0012**0.0017**

SV_(pre→2W,high,high)(rCBV, K^(trans), 0.6)^(x) 0.0199* 0.0687 0.0012**0.0015**

μ_(pre→2W)(rCBV) 0.0049** 0.2336 0.1086 0.0066**

μ_(pre→2W)(K^(trans)) 0.5233 0.1704 0.6704 0.8775

GTV_(pre→2W) 0.1086 0.0653 0.0039** 0.0124* Abbreviations: GTV = grosstumor volume, R = responders; S = stables; NR = non-responders; ^(x) Theoptimum value of α is 0.6, see the results of the ROC analysis *P <0.05; **P < 0.01.

indicates data missing or illegible when filed

Predictive Values of the Physiological Parameter Defined Subvolumes:

the predictive value of the decrease in the subvolumes of the tumorsdefined by the high rCBV/K^(trans) from Pre-RT to 2 W for predictingresponsive tumors post-RT was explored and compared the performance withthe two conventional metrics. The ROC analysis showed that the AUCs were0.80±0.07 (±SEM), 0.70±0.08, 0.67±0.08 and 0.56±0.09 for {circumflexover (Δ)}SV_(Pre→2W,high)(rCBV), {circumflex over (Δ)}μ_(Pre→2W)(rCBV),{circumflex over (Δ)}GTV_(Pre→2W) and {circumflex over(Δ)}SV_(Pre→2W,high)(K^(trans)), respectively (as shown in FIG. 4A),indicating the high-rCBV defined subvolume of the tumor had the bestperformance among the tested variables for predicting responsive tumor.The subvolumes defined by the high-rCBV and high-K^(trans) classes withthe weighting factor 0.6, determined by empirical evaluation of the AUCs(as shown in FIG. 4B), resulted in the largest AUC, 0.85±0.06. Thestatistical analysis of the pair-wise ROC curves revealed that{circumflex over (Δ)}SV_(Pre→2W,high,high)(rCBV, k^(trans), 0.6) was apredictor slightly but not significantly better than {circumflex over(Δ)}SV_(Pre→2W,high)(rCBV)(P>0.18), or {circumflex over(Δ)}μ_(Pre→2W)(rCBV)(p>0.0574). However, both {circumflex over(Δ)}SV_(Pre→2W,high)(rCBV) and {circumflex over(Δ)}SV_(Pre→2W,high,high)(rCBV, k^(trans), 0.6) were predictorssignificantly better than {circumflex over (Δ)}GTV_(Pre→2W) (p=0.022 andp=0.0102, respectively). Finally, the predictive value of {circumflexover (Δ)}μ_(Pre→2W)(rCBV) was slightly but not significantly better than{circumflex over (Δ)}GTV_(Pre→2W) (p>0.41).

Thus, the present techniques propose new approaches to delineating theabnormality subvolumes of a tumor defined from physiological, metabolic,and/or biological imaging parameters and relating their early changes totreatment response in the patients who have had brain metastases treatedby WBRT. The approaches analyze the heterogeneous distributions ofphysiological, metabolic, and/or biological imaging parameters of thetumors, then assigns each voxel of the tumor a probabilistic membershipfunction belonging to the various physiological, metabolic, orbiological classes defined in a sample of tumors, and finally calculatesthe related subvolumes in each tumor.

The framework presented herein can be applied to other physiological,metabolic or molecular images, including but not limited to apparentdiffusion coefficient and 11 C-Methinion positron emission tomography(PET), to delineate a different physiological-parameter definedsubvolume of a tumor. A subvolume of the tumor defined in such way couldbe a candidate for a radiation boost target volume, for example.

Experiment 2

In another example implementation the present techniques were applied todynamic contrast enhanced (DCE) MRI image data. The implementation usedglobal initiated regularized local fuzzy clustering (GRELFC), similar tothe techniques outlined above, to identify abnormal subvolumes of headand neck cancers (HNC) from heterogeneous distributions of tumor bloodvolume (BV) and blood flow (BF), i.e., from functional image data forassessment of therapy response.

In operation, BV and BF images, derived from DCE-MRI, of 14 patientswith advanced HNC were obtained before chemo-radiation therapy(chemo-RT) and 2 weeks after start of 7-week chemo-RT. The delineatedsubvolumes of tumors with low BV or BF before and during treatment wereevaluated for their associations with local failure. Receiver operatingcharacteristic (ROC) analysis was used to assess performance of themethod for prediction of local failure of HNC. As discussed further, theresults showed that the sizes of the subvolumes of primary tumors withlow BV before and during treatment were significantly greater in thepatients with local failure (LF) than with local control (LC) (p=0.02and 0.01, respectively). While the total tumor volume reductions duringtreatment were similar for the patients with LF and LC, the reductionrate of the size of the subvolumes of the primary tumor with low BV inresponse to 2 weeks of chemo-RT was significantly slower for thepatients with LF than those with LC. The subvolumes of the tumor withlow BV before and during treatment have greater specificity forprediction of local failure, with a given sensitivity, than thepre-treatment total tumor volume, the percentage change in the tumorvolume during treatment, or the change in the mean value of BV over thewhole tumor during chemo-RT.

Standard care for advanced head-and-neck cancers (HNC) includesaggressive concurrent chemo-radiation therapy (chemo-RT). Intensifyingthis regimen has resulted in improved control rates as well as increasedrates and severity of late toxicity. Despite improvements, failure ratesare 20-50% in patients who are negative for human papillomavirus andfailures are predominantly local-regional. The present techniquesprovide a means for prognostic or predictive valuation to facilitateidentification of subvolumes of the tumors likely to be resistant toconventional radiation doses in the patients who are at high-risk forlocal-regional failure and who thus may benefit from intensifying localtreatment. Recently, functional imaging that assesses tumor hypoxia orperfusion prior to therapy in HNC has been described. In patients withHNC treated by RT, low tumor perfusion prior to RT and T-stageclassification were identified as independent predictors for localfailure, suggesting that poorly perfused HN tumors respond poorly to RT,and that pre-RT tumor perfusion provides prognostic value for localcontrol even when accounting for established clinical prognosticfactors. Another recent study showed that high pre-therapy tumor bloodvolume and perfusion were associated with large decreases in tumorvolumes in response to induction chemotherapy. By characterizing tumorproperties of blood volume (BV) and blood flow (BF) derived from DCE-MRIimages, the present techniques showed that an increase of blood volumein the primary tumor volume during the early course of chemo-RT wasassociated with local control, which indicates that poorly perfusedtumors may be resistant to conventional doses of radiation therapy.

In these previous studies, the average BV and BF in the entire tumorvolume were investigated for their association with treatment outcome.However, many advanced head-and-neck cancers have quite inhomogeneousperfusion characteristics within the tumor volume, particularly in largetumors. Thus averaging BV and BF values over the entire tumor may notresult in optimal parameters for prediction of outcomes. A change in thetotal tumor volume during the course of chemo-RT, although the totaltumor volume in HNC is a clinical prognostic factor, has a poorpredictive value for outcomes. The subvolume of the tumor that isresistant to chemo-RT might provide better prediction for outcomes.Therefore, it was hypothesized that the large poorly perfused subvolumeof the head-and-neck tumor pre-therapy and persisting during the earlycourse of definitive chemo-RT could be a better indicator forlocal-regional treatment failure than the tumor size or the averageperfusion parameters over the entire tumors.

Identification of Subvolumes of the Tumor:

the technique implemented in this example was designed to globallyinitiate training to identify fuzzy clusters of the physiologicalimaging parameters in the feature space, and then classify each tumorvolume with local regularization to subvolumes according to the globalfeature clusters. The technique is designed not only to identify thesubvolumes of individual tumors based upon the heterogeneousdistributions of physiological imaging parameters but also to be able tocompare the classified subvolumes of the tumors across patients and overmultiple time points. The fuzzy clustering method, specifically fuzzyC-means clustering (FCM), chosen in the GRELFC method aims to deal with(1) intrinsic variations of the physiological parameters in the tumors,(2) partial volume effects due to the limited resolution of imagingsources, and (3) uncertainty due to noise. FCM clustering is a method ofunsupervised learning to assign a set of observations to belong tosubsets (clusters) with probability memberships. To partition a set ofobservations {x_(k)}, e.g., image voxels, into c clusters, an objectivefunction with local spatial regularization,

$\begin{matrix}{J_{m} = {{\sum\limits_{i = 1}^{c}{\sum\limits_{k = 1}^{N}{u_{ik}^{m}{{x_{k} - v_{i}}}^{2}}}} + {\alpha {\sum\limits_{i = 1}^{c}{\sum\limits_{k = 1}^{N}{u_{ik}^{m}{{{\overset{\_}{x}}_{k} - v_{i}}}^{2}}}}}}} & (8)\end{matrix}$

is to be minimized. In Eq. 8, the first term is a standard FCM costfunction and the second term provides a spatial constraint to overcomethe effect of image noise and to improve spatial connectivity. Hereu_(ik) is a probabilistic (fuzzy) membership of observation xk belongingto class i, v_(i) is a prototype vector of class i, x_(k) is a mean ormedian value of neighbors of voxel k, m defines fuzziness of themembership, and α is a weighting factor of spatial constraints. A 2D or3D kernel, depending upon image resolution, can be used to defineneighbors of each voxel for spatial constraint. Solutions that minimizethe objective function of Eq. 8 are given by:

$\begin{matrix}{{u_{ik} = {\frac{\left( {{{x_{k} - v_{i}}}^{2} + {\alpha {{{\overset{\_}{x}}_{k} - v_{i}}}^{2}}} \right)^{- \frac{1}{({m - 1})}}}{\sum\limits_{j = 1}^{c}\left( {{{x_{k} - v_{j}}}^{2} + {\alpha {{{\overset{\_}{x}}_{k} - v_{j}}}^{2}}} \right)^{- \frac{1}{({m - 1})}}}\mspace{14mu} {and}}}{v_{i} = \frac{\sum\limits_{k = 1}^{n}{u_{ik}^{m}\left( {x_{k} + {\alpha \; {\overset{\_}{x}}_{k}}} \right)}}{\left( {1 + \alpha} \right){\sum\limits_{k = 1}^{n}u_{ik}^{m}}}}} & (9)\end{matrix}$

which are solved iteratively until reaching a stopping criterion. Thevalues for m and α are usually determined empirically. The analysis canbe applied to either single- or multiple-component parameters.

In order to evaluate longitudinal changes in physiological imagingparameters of interest in the tumor, a set of data was used as trainingdata to determine definitions of clusters (prototype vectors andrelationships between fuzzy memberships and observations), and then theremaining sets of data are partitioned according to the classdefinitions of training data. To avoid a bias from large tumors intraining data, each of the tumor volumes is up-sampled or down-sampledto have an equal number of voxels contributing to the training datawhile maintaining the initial distribution of the physiological imagingparameters from the original into the re-sampled tumor. To do so, ahistogram of the physiological imaging parameters of each tumor isgenerated, and re-sampled to create a new tumor volume with the samesize. The re-created tumor volume, while preserving the originaldistribution (histogram) of the imaging parameters, cannot maintain theoriginal spatial relationship between voxels, which is not critical fortraining data to determine prototype vectors of global clusters. Topartition individual tumors in the second data set, fuzzy membership ofeach voxel of each tumor is classified using the prototype vectors foundin analysis of the training data by Eq. 9, where spatial constraint isused to improve spatial continuity. Finally, the highest probability offuzzy membership of each voxel is used to assign the voxel to a discreetclass. As a result, the tumor is partitioned into spatial subvolumesbased upon the similarity of the physiological parameters of interest.The temporal changes in partitioned subvolumes of the tumor areevaluated for their association with outcomes. To evaluate this methodto identify significant subvolumes of the tumor related to outcomes, themethod to BV and BF images derived from DCE-MRI of patients withadvanced head and neck cancer was applied.

Patients and Treatment:

14 patients with advanced squamous cell carcinomas were enrolled in aprospective MRI study that was approved by the institutional reviewboard at the University of Michigan. Informed consent was obtained fromall patients. Table 3 summarized the characteristics of the patients.All patients received definitive 7-week concurrent chemo-RT with a totalradiation dose of 70 Gy to the primary gross tumor volume (GTV) andinvolved nodes, by intensity-modulated radiation therapy (12 patients)or three-dimensional conformal radiation therapy (2 patients). Forchemotherapy, 8 patients received carboplatin (1 area under the curve)and paclitaxel 30 mg/m2 weekly; 5 patients received cisplatin 100 mg/m2once every 3 weeks; and 1 patient received cetuximab with a loading doseof 400 mg/m2 followed by weekly dose of 250 mg/m².

TABLE 3 Patients Characteristics - Example 2 MRI Scans Age(y)/ DiseasePre/during/ Patients Sex Location Stage 3 Mpost Outcome 1 65/F Softpalate T2N3 y/y/n LRF/Dead 2 62/M Tonsil T1 N2a y/y/y LRC 3 58/MHypopharynx T4 N2b y/y/y LF/Dead 4 83/M Larynx T4N0 y/y/n LF/Dead 5 61/FTonsil T2N3 y/y/y RF 6 43/M BOT + tonsil T4N2c y/y/n LF/Dead 7 49/MTonsil T4N2c y/y/y LRC 8 62/M BOT T3N0 y/y/y LRC 9 39/M UNP TxN3 y/y/yLRC 10 57/M Nasopharyneal T2N2b y/y/y LRC 11 58/M piriform sinus T1N2cy/y/y LRC 12 42/M Tonsil T2N2b y/y/y LRC/DF 13 62/M Tonsil T2N2b y/y/yLRC 14 58/M Tonsil T2N2b y/y/y LRC Abbreviations: F = Female; M = male;Pre = pre RT; during = week 2 during the course of RT; 3 Mpost = threemonths after the completion of RT. BOT = base of tongue; UNP = unknownprimary disease; LRF: local-regional failure: LRC = local-regionalcontrol; LF = local failure; RF = regional failure; DF = distantfailure.

After receiving chemo-RT, all patients were followed for clinicalevaluation. Follow-up visits per protocol took place every 6 weeks forthe first two years, then every 3 months for the third year, and every 6months from the fourth year forward. Per protocol, MRI scans took place3 months after the completion of RT. FDG positron emission tomography(PET), CT, other MRI scans, or biopsy was elected as clinicalindication. The median follow-up time for living patients was 19.6months (range 14.1 to 36.4 months) post treatment. At these evaluations,8 patients had local-regional controlled diseases and without distantmetastasis, 3 had local failure, 1 had local-regional failure, 1 hadregional failure, and 1 had local-regional control but distant failure.

MRI Acquisition:

all MRI scans were acquired using a Philips 3T scanner (PhilipsHealthcare). Scans were taken before radiation therapy (“pre-RT”) and 2weeks after start of therapy (later referred as “wk-2” or “during-RT”).Each scan included the following series: T1-weighted images, T2-weightedfluid-attenuated inversion recovery (FLAIR) images, DCE T1-weightedimages, and post-contrast T1-weighted images. Thirty-two dynamic volumesof T1-weighted MRI were acquired by a 3D gradient echo pulse sequence inthe sagittal plane during intravenous injection of 0.1 mL/kg Gd-DTPAwith TR/TE=5.1/1.1 ms, flip angle=20 degrees, temporal resolution=7.6 s,and voxel size=2×2×2 mm3 to cover the whole head and neck includingprimary tumor and involved node.

Gross Tumor Volume (GTV) Definition:

Gross Tumor Volumes (GTVs) were delineated on the post-contrastT1-weighted images acquired pre-RT and during-RT by a head-and-neckradiation oncologist. If available, treatment planning CT and diagnosticPET scans were referenced. The primary and nodal GTVs were drawnseparately due to the possibility that the two could respond to therapydifferently. Heterogeneous BV and BF of the primary GTV for predictionof local control (LC) or local failure (LF) were investigated.

Quantification of BV and BF:

DCE images were fitted to the modified two-compartment Tofts model. Themodel assumes the contrast agent concentration Ct(t) in the tissue attime t following the equation below:

$\begin{matrix}{\mspace{79mu} {{{C_{i}(t)} = {{K^{trans}\text{?}^{- {k_{ep}{({t - \tau})}}}\ {C_{p}(\tau)}{\tau}} + {v_{p\;}{C_{p}(t)}}}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (10)\end{matrix}$

where Cp(t) is the artery input function, K^(trans) is the volumetransfer constant from the plasma to the extravascular extracellularspace (EES), k_(ep) is the efflux rate constant from the EES to theplasma, and vp is the fractional volume of the blood plasma. BV iscalculated from v_(p) by BV=v_(p)/(1−Hct) (Hct is the hematocrit) andconverted to per unit mass of the tissue.

BF images were derived by using the method described by Mullani et al.and Hermans et al. described by the following equation:

$\begin{matrix}{\left( \frac{{C_{i}(t)}}{t} \right)_{\max} = {F\; \rho \; {C_{p}(t)}_{\max}}} & (11)\end{matrix}$

where ρ is the density of tissue, F is the blood flow, and(dCt/dt)_(max) is the maximum derivative of the contrast concentrationuptake in the tissue.

An example of post-contrast T1, T2 FLAIR, BV, and BF slices is shown inFIG. 5.

Clustering Analyses:

all image analyses were completed using an in-house software package:Functional Image Analysis Tool (FIAT). For analysis of the singleparameter of BV, first training data were generated from thepre-treatment BV values of the primary GTVs of all patients. Each of thepre-RT primary GTVs was re-sampled to create 10,000 voxels according tothe distribution of BV values within the original GTV. This experiencesuggested that 10,000 voxels per tumor were sufficient to maintain thedistribution of BV in the tumor. As a result, the training dataconsisted of 100×100×14 voxels (14 is the number of patients), and thenwas partitioned into 2, 3, or 4 feature clusters using the abovedescribed clustering method. The resulting prototype vectors {v_(i)}were adopted to partition each individual GTV before and during therapyto obtain the fuzzy membership of each voxel belonging to the featureclasses. The subvolumes of the primary GTVs defined by the clusters withlow BV before and during RT were tested for their association with localfailure using the Mann-Whitney U test. A two-tailed p-value<0.05 wasconsidered significant. The same analysis was applied to BF data.

To evaluate the discriminatory value of BF combined with BV, {x_(k)} inEq. 8 was formed to have two components, BV and BF, which were weightedequally for their contributions. The two-component dataset {x_(k)} wasanalyzed similar to the single parameter. FIG. 6 illustrates the processof classification of the whole GTV into subvolumes based upon theircharacteristic physiological features and the analysis in thetwo-component feature space defined by the two parameter maps, BV andBF. The white contour depicts the GTV, and the two-dimensional featurespace is depicted with each voxel from the two parameter maps isprojected based upon their values. As shown in FIG. 6, the voxels arepartitioned into three clusters (e.g., triangles, circles, and squares)using FCM clustering analysis which optimizes homogeneity of theparameters within the cluster and separation between the clusters.

Two representative examples of the subvolumes of the primary GTVs withlow BV, pre-RT, and at week 2 are illustrated in FIGS. 7A and 7B. The BVmaps are color-coded and overlaid on post-Gd T1-weighted images so thatthe white contours and blue color represent primary GTV and thesubvolumes of the GTV with low BV, respectively. FIG. 7A depicts a localfailure case that includes a whole GTV of 61.4 mL and a subvolume of thetumor with low BV of 28.6 mL at pre-RT (left portion of FIG. 7A) and44.8 mL and 20.9 mL at week 2 (right portion of FIG. 7A), respectively.FIG. 7B depicts a local control case with a whole GTV of 97.5 mL and asubvolume of the tumor with low BV of 16.5 mL at pre-RT (left portion ofFIG. 7B) and 52.3 mL and 17.4 mL at week 2 (right portion of FIG. 7B),respectively.

Receiver Operating Characteristic (ROC) Analysis:

to evaluate performance of the subvolumes of the tumors identified fromBV, BF or combination of BV and BF for prediction of local failure,receiver operating characteristic (ROC) analysis was performed usingsoftware package ROCKIT. The fitted ROC curves and the areas under thecurves (Az) of several representative metrics were generated andcompared.

Results; Subvolumes of the Primary GTVs with Low BV Pre and DuringTreatment:

the subvolumes of the primary GTVs derived from clustering analysis ofthe heterogeneous distribution of BV were assessed and were related tolocal control (LC) and local failure (LF). When the primary GTVspre-treatment were partitioned into two classes based upon the BVdistribution, the subvolumes of the primary GTVs with low BV in thepatients with LC, ranging from 2.4 to 26.6 mL with a median of 9.9 mL,were significantly smaller than those in the patients with LF (p<0.02),from 15.0 to 46.0 mL with a median of 31.9 mL in the patients with LF asshown in Table 4 and in FIG. 8. After receiving 2 weeks of the 7-weekchemo-RT treatment course, the subvolumes of the GTVs with low BVdecreased to 0.3 to 17.4 mL with a median of 3.7 mL in the patients withLC, and changed to 7.7 to 49.9 mL with a median of 23.8 mL in thepatients with LF. The persistence of large subvolumes of the GTVs withlow BV during-treatment differentiated LF from LC tumors significantly(p<0.01) (Table 4), suggesting that a large subvolume of poorly perfusedtumor both initially prior to therapy and persisting during the earlycourse of chemo-RT may be an indicator for local failure. FIG. 8 depictsthe correlation of the subvolumes with low BV at the two time-points(i.e., pre-RT vs. during-RT) and shows that the subvolumes at these twotime-points are highly correlated (e.g., p=0.96). As shown in FIG. 8,the scatter plot of the subvolumes of the primary GTVs with low BV preand during chemo-RT shows that three of the four primary tumors with LFhave the largest subvolumes with low BV.

TABLE 4 Summary of Tumor BV and BF Analysis Results Median (Range) LocalControl Local Failure P- Parameters (n = 9) (n = 4) value BV subvolumeof tumor with low BV 9.9 (2.4 to 26.6) 31.9 (15.0 to 46.0) 0.02subvolume pre-RT [mL] subvolume of tumor with low BV 3.7 (0.3 to 17.4)23.8 (7.7 to 49.9) 0.01 wk 2 [mL] BF subvolume of tumor with low BF 13.0(5.2 to 32.1) 35.5 (17.1 to 67.9) 0.07 subvolume pre-RT [mL] subvolumeof tumor with low BF 6.3 (0.9 to 21.2) 20.8 (9.6 to 39.6) 0.05 wk 2 [mL]BF + subvolume of tumor with low 10.5 (4.1 to 28.9) 32.2 (16.3 to 52.9)0.03 BV BV + BF subvolume subvolume of tumor with low 4.4 (0.4 to 18.6)21.6 (8.4 to 45.6) 0.01 BV + BF GTV primary GTV Pre-RT [mL] 15.8 (5.3 to97.5) 74.3 (19.6 to 120.1) 0.05 % change in GTV −27.9 (−65.4 to −11.2)−21.4 (−44.6 to −8.2) 0.33 (wk 2 vs. pre-RT) Mean change in mean tumorBV 5.1 (−0.6 to 13.2) 1.0 (−1.7 to 1.6) 0.03 BV (wk 2 vs. pre-RT)[mL/100 g] Mean change in mean tumor BF 20.1 (−3.9 to 54.6) 15.0 (−22.1to 41.1) 0.71 BF (wk 2 vs. pre-RT) [mL/100 g·min] All subvolumes abovewere determined by the analysis for two clusters.

The fractional reduction (or reduction rate) of the subvolumes in theprimary GTVs with low BV during-treatment vs. pre-treatment wassignificantly greater in the patients with LC than those with LF, 56%±9%and 23%±12% respectively (p<0.05). In the patients with LF, the initiallarge subvolumes of the primary GTVs with low BV and slow response ratesof the subvolumes to two weeks of chemo-RT suggest that locallyintensified treatment is required to further reduction of thispersisting and aggressive subvolume of the tumor.

Sub Volumes of the Primary GTVs with Low BF Pre and During Treatment:

it was assessed whether the subvolumes of the primary GTVs identified bythe heterogeneous distribution of BF differentiated tumors with LF fromLC, either analysis of BF independently or combining BF with BV, to testthe discriminatory value of BF in comparison with BV. Analysis of BFalone yielded a similar trend as BV: prior to chemo-285 RT, the patientswith LF had large subvolumes of the primary GTVs with low BF (range:17.1 to 67.9 mL; median: 35.5 mL) while those with LC had smallsubvolumes with low BF (range: 5.2 to 32.1 mL; median: 13.0 mL). Thedifference between the two groups was not significant pre-treatment(p=0.07). After 2 weeks of treatment, the subvolumes of the primary GTVswith low BF decreased to 9.6 to 39.6 mL with a median of 20.8 mL in thepatients with LF and to 0.9 to 21.2 mL with a median of 6.3 mL in thepatients with LC, the difference of which was marginally significant(p=0.05) (Table 4). While analyzing BF with BV together, there was noimprovement in the differentiation of the LF from the LC tumors,compared to analysis of BV alone (Table 4).

Predictive Value of the Subvolumes of the Primary GTVs with Low BV forLocal Failure:

the predictive value of the subvolumes of the primary GTVs with low BVpre and during treatment for local failure was explored using ROCanalysis, and its performance was compared with other conventionalmetrics, such as the pre-treatment tumor volume, the percentage changein tumor volume during-treatment, and the change in a mean BV value overthe whole tumor during-treatment. The areas under the ROC curves (Az), ameasure of overall performance of a metric for prediction of an event(e.g., local failure), indicate that all BV-related (function-based)metrics have better performance than the tumor volume metrics(anatomy-based). FIG. 9 depicts a comparison of fitted ROC curves offive metrics for prediction of local failure. Specifically, the areasunder the ROC curves were 0.872±0.098 for the pre-treatment tumor volumeand 0.723±0.158 for the 305 percentage tumor volume changeduring-treatment. However, the change in the mean of tumor BV valuesduring-treatment, a valuable functional parameter for prediction oflocal failure reported previously, had an area under the ROC curve0.903±0.084. When considering the subvolume of the primary GTV with lowBV, the areas under the ROC curves increased to 0.925±0.107 forpre-treatment and 0.947±0.079 for during-treatment. The ROC analysisindicates that 85% sensitivity in predicting local failure resulted in87.5% and 91.0% specificity by the subvolumes of the GTVs with low BVbefore and during RT, respectively, compared with 83.0% specificity bythe change in the mean BV values over the entire GTV during-treatmentand 75.5% specificity by the pre-treatment GTV.

Subvolumes of the Nodal GTVs with Low BV:

the subvolumes of the nodal GTVs with low BV were explored. There weretwo cases with regional failure. Prior to treatment, the subvolumes ofthe nodal GTVs with low BV were 35.5 and 120.1 mL in the two patientswith regional failure, which were greater than those with regionalcontrol, ranging from 4.0 to 29.5 mL with a median of 15.6 mL. After 2weeks of chemo-RT, the subvolumes of the nodal GTVs with low BV changedto 11.6 and 132.5 mL in the patients with regional failure, and to amedian of 9.9 mL with a range from 3.3 to 24.5 mL in those with regionalcontrol. It is worthwhile to point out that the patient who hadlocal-regional failure had the largest subvolume of the nodal GTV withlow BV (˜120 mL) and the smallest subvolume of the primary GTV with lowBV among the LF cases (˜15 mL as shown in FIG. 8), and also the primaryand nodal GTVs were anatomically adjacent or connected, which mightexplain why this case deviated from other local failure cases as shownFIG. 8.

FIG. 10 depicts a flow diagram illustrating an exemplary method forpredicting treatment outcome utilizing the vascular properties of atumor or disease. The method 1000 begins with the system obtainingN-number of voxel-based physiological imaging-derived parameters of asample of tumors or other diseases (block 1002). Using these samples,the system generates a n-dimensional probability density function orhistogram of these parameters of samples (block 1004), and then createsa abnormality probability function from the n-dimensional PDF usingfuzzy logic analysis or other discrimination analysis (block 1006). Themethod 1000 also includes creating a n-dimensional PDF of the parametersof a new tumor or disease (block 1008) and applying the membershipclassification to the created n-dimensional PDF of the new tumor ordisease to obtain the abnormality probability function of each voxel ofthe tumor or disease (block 1010). The system creates an abnormalityprobability map of the tumor or disease as a visual output or target orfocal therapy (block 1012). The method 1000 includes creating aquantitative metric from the abnormality probability function of thetumor or disease for diagnosis, prognosis, or prediction in treatment(block 1014).

In this example, a method was developed and investigated to identifysubvolumes of the tumors by characterizing the heterogeneous tumor bloodvolume and blood flow before and during treatment using DCE-MRI inpatients who had advanced HNC and were treated with concurrent chemo-RT.The subvolumes of the primary GTVs with low BV or BF were related to theoutcomes. Also, the predictive value of the subvolumes of the primaryGTVs with low BV was explored for local failure and compared these withother quantitative metrics derived from anatomic or functional imaging.It was discovered that the large subvolumes of the primary GTVs with lowblood volume pre-treatment and persisting during the early course ofchemo-RT are associated with high probability of local failure. For agiven sensitivity for prediction of local failure, the tumor subvolumewith low blood volume has higher specificity compared to the change inthe mean of blood volume values over the whole tumor during-RT, thepercentage change of tumor volumes during-RT, or the pre-RT GTV volume.The tumor subvolume with low blood volume might be a better biomarkerfor identifying the patients with advanced HNC at high-risk for localfailure and for defining a radiation boost target to intensify localtreatment.

Many large tumors manifest intra-tumor heterogeneity, e.g., multiplephenotypes within a single tumor, which is possibly responsible forheterogeneous treatment response within the tumor. It is plausible thata portion of the tumor is more aggressive or more resistant totreatment, and thereby, might ultimately determine the treatmentoutcome. However, the mean value or the change in the mean value of afunctional imaging parameter over a heterogeneous tumor has generallybeen used to correlate with treatment outcome in most previous imagingstudies, which could compromise the predictive power of the parameter.In this example, in aiming to assess the tumor heterogeneous perfusionand blood volume, and it was found the poorly perfused subvolumes priorto and during chemo-RT have greater specificity for prediction of localfailure for a given sensitivity than considering the tumor as a whole(FIG. 8).

It is a challenge to characterize tumor heterogeneity and heterogeneouschanges over a longitudinal study. One approach to address the problemis to analyze statistical differences at each voxel over time. Thisrequires a reliable and accurate voxel-to-voxel alignment of imagesacquired at different time points. The volume, shape and image intensityof the tumors may change over weeks and months. For head-and-neckcancers, the patient position changes due to the neck flexibility overscans can result in additional difficulty for voxel-level imagealignment, even with highly sophisticated deformable image registrationtools. In addition, voxel-level analyses show large uncertainty and poorreproducibility, which impact on the correlation with response andoutcome. Therefore, in this study, a robust technique (clusteringanalysis) was developed to characterize heterogeneous vascular andperfusion parameters of head-and-neck tumors as well as their changesover time. This technique can map functional images from different timepoints into a feature space, and then analyzes the intrinsic propertiesof the parameter of interest by statistically grouping and splitting thevoxels into feature classes (e.g., the voxels with low or high bloodvolume) based upon their similarities and differences. This techniquecan be applied to imaging data from longitudinal studies without therequirement of voxel-size accuracy of image registration. The techniquecan be generalized to other tumors and other imaging parameters. Also,fuzzy clustering used in the method allows the system to manage thecontinuous distribution of the physiological imaging parameters in thetumor as well as noise in images. Through this work, it was demonstratedthat the potential value of the feature class (the subvolume of thetumor with low blood volume) delineated by this method for prediction ofoutcome and definition of potential radiation boost target volumes.

Several methods have been proposed to automatically segment tumorvolumes (binary segmentation) as well as for identification ofinhomogeneous tumor subvolumes (multiple classes) from metabolic PETimages, e.g., watershed and fuzzy local adapted Bayesian (FLAB)segmentation methods. These methods have focused on how to deal with theinfluence of object (tumor) size on radioactivity detected by PET due tolimited spatial resolution, by iterative segmentation using phantomscans for guidance. Recently, FLAB segmentation has been extended toidentify multiple classes in the inhomogeneous FDG distribution in thetumors. However, this method needs to pre-define the fuzzy level, andbecomes complicated when dealing with 3 or more classes and variousobject sizes, especially for small objects (<2 cm). Most importantly,these methods cannot handle the longitudinal data. The questions thatthese methods attempt to address are generally not major concerns inMRI. In this example, it was discovered that the large poorly perfusedsubvolume of the HNC both before and during the early course of chemo-RTwere associated with local failure. Also, these large subvolumes oftumors with low BV had a slow reduction rate in response to earlytreatment, suggesting local intensification of treatment may be neededto sufficiently reduce this persisting and aggressive portion of thetumors. Information provided by the early-course scans confirmativelysupport the findings prior to therapy, which could increase theconfidence of clinical decision-making based upon the pre-treatmentscans alone. Also, a large reduction in the subvolume of tumor with lowblood volume during the early course of chemo-RT could be used as anindicator for a decreased-intensity treatment in the patients who havegood outcomes in order to reduce normal tissue toxicity. These findingsand reproducibility of test and retest of DCE-MRI without therapy willbe further investigated in a large cohort study.

B. Principal Component Analysis in Subvolume Analysis

The subvolume analysis method of FIG. 10 may also use PC featuresderived from PCA as another input. The PCA may include a pharmacokineticmodel free framework that analyzes the DCE-MRI data. This PCA techniquemay also utilize DCE-MRI data from image voxels of a sample of braintumors to construct a DCE matrix. PCA is then applied to generate thePCs and subsequent projection coefficient maps. Next, a modelingtechnique, such as a pattern recognition based upon fuzzy-c-meansclustering, may be used to delineate the tumor subvolumes relating tothe value of the projection coefficients. The relationship betweenchanges in different tumor subvolumes and treatment response may beevaluated to differentiate responsive from stable and progressivetumors.

This PCA technique may use a general framework to derive aresponse-predictor from DCE-MRI data without using the PK modelingtechniques and a general framework to include a semi-automated or fullyautomated tool for response assessment and therapy guidance in radiationtherapy of brain metastases. In this PCA technique, the system maytransfer the DCE curves into an N-dimensional feature space usingprinciple component analysis, may identify the most response-relatedfeatures using a FCM-based technique, and may combine the features todefine the tumor subvolumes.

In some examples, this PCA technique contains two phases: a developmentphase and a usage phase. In the development phase, a sample of the DCEdata from brain metastases is processed and analyzed to develop themodel and the predictive metric. In the usage phase, the PCA techniquedetermines if the predictive metric could be extracted rapidly from theDCE data of a new patient scan.

Experiment 3

Using the data gathered from Experiment 1, the following steps of thePCA technique were employed.

Pre-Processing; DCE Curves Normalization:

the dynamic curve at each voxel represents the temporal changes insignal intensity after the contrast injection. The signal intensitychange AS from pre (baseline) to post contrast is calculated asfollowing:

$\begin{matrix}{{\Delta \; {S(t)}} \equiv \frac{{s(t)} - s_{o}}{S_{o}}} & (12)\end{matrix}$

where S(t) and So represent signal intensities of a DCE curve at times tand 0 (the time of contrast injection), respectively. Note that AS(t) isproportional to AR1, the change in the longitudinal relaxation rate, aslong as TR×R1<<1. To account for the individual hemodynamic response tocontrast, AS was normalized at each voxel using the peak of the arterialinput function, AIFmax, obtained during the same scan as:

$\begin{matrix}{{\Delta \; {S_{N}(t)}} = {\Delta \; {{S(t)} \cdot \frac{1}{{AIF}_{\max}}}}} & (13)\end{matrix}$

An arterial input function can be determined from a region of interest(ROI) in a large artery (e.g., carotid artery, etc.) manually,semi-automatically or automatically. In the current work, a region ofinterest containing brain and neck is initially contoured. DCE curveswithin the contour are averaged to determine the peak enhancement,T_(max), in the tissue. Assuming that the arterial input functionreaches the enhancement peak prior to tissue, the first 20 voxels withthe maximum enhancement in AS(Tma, −At), one time frame before Tniox,within the contour are thresholded, and then the corresponding DCEcurves are averaged to be considered as an arterial input function.

DCE Curve Reconstruction:

the DCE curves in each scan may not be acquired with the exactly sametemporal resolutions and time durations. Thus, the DCE curves may bestandardized in such a way that all curves have the same temporalresolution and length. The spline curve-fitting method may be used toreconstruct each DCE curve, and resample the curves to have a temporalresolution of 4 sec and a total length of 120 sec, respectively.

DCE Curve Alignment:

the DCE curves from voxels within the tumor volumes of all patients mayneed to be temporally aligned for further processing. The arterial inputfunction (AIF) obtained from each patient scan may be used to align theDCE curves of voxels in the tumor volumes. The Gamma variate functionwas fit to each AIF as follows:

$\begin{matrix}{g = \left\{ {{\begin{matrix}{\left( {t - t_{0}} \right)^{\alpha}\exp^{- {\beta {({t - t_{0}})}}}} & {t \geq t_{0}} \\0 & {t < 0}\end{matrix}{AIF}} = {{g(t)} + {\lambda {\int_{0}^{t}{{g\left( {t - t} \right)}\ {t}}}}}} \right.} & (14)\end{matrix}$

All AIFs are then aligned at t₀ that is resigned to be time 0. Using theresultant time shifts, the DCE curves from each scan are adjustedaccordingly. This process makes all DCE curves aligned based on thestart of enhancement in the arterial input function.

Projection Coefficient Map from Karhumen-Loeve Expansion of DCE Curves:

the primary goal is to extract response-predictive features rapidly anddirectly from the DCE curves. Thus, the DCE curves were expanded using aset of basis functions, by which the coefficients of the projectionvectors for each DCE curve is a unique representation in a new space. Inthe development stage, using DCE data from a sample of brain metastases(e.g., pre-therapy DCE data), the matrix C (N×T) was constructed inwhich each row represents a DCE curve from one voxel in the tumors. N isthe total number of voxels in all tumors and T is the number of timepoints in each curve. Principal component analysis (PCA) was applied toC to obtain a complete set of a total of T orthonormal principalcomponents (PC_(i)). The Karhumen-Loeve transformation of each DCE curvewas performed, S_(N), in each voxel of the tumor to:

ΔS _(N)=Σ_(i=1) ^(T)α_(i) PC _(i) →ΔS _(N)≡(a ₁ , a ₂ , . . . , a_(T))  (15)

where a_(i) is the projection coefficient (cPC) corresponding to the ithprincipal component. Each DCE curve in a tumor volume is representeduniquely by {α_(i)} in a T-dimensional coefficient space. However, Eq.(15) can be truncated at the first M principal components which contain99% of energy of the original DCE curves.

Projection Coefficient Defined Tumor Subvolumes; Probability DensityFunction of a Projection Coefficient in a Tumor:

each PC depicts a feature of the tumor DCE curve. Each voxel in a tumorhas a unique projection coefficient on each PC. For each PC, theprojection coefficients of the voxels in a tumor, which can be presentedas a volumetric map of a lesion, have a distinct role in predicting thetreatment response and outcome. The distribution of the projectioncoefficients in a large tumor is heterogeneous, similar to thephysiological parameters. Similar to what has been done previously forthe physiological parameters, the distribution patterns of a projectioncoefficient, a_(i), in the lesions were analyzed, and subsequent changesduring treatment. A PDF or histogram of a_(i) of a lesion is generatedusing a non-parametric PDF estimator. The PDF consists of 150evenly-spaced points to cover the range of a_(i) for all the lesions ofinterest. A value of the PDF at a point x, H(a_(i)=x), of a lesion iscalculated as:

H(a _(i) =x)≡n _(i) : x−ε≦a _(i) <x+ε  (16)

where n_(i) is the number of voxels within |a_(i)−x|<ε, and ε is asmooth factor of H and set as ε=σ/4 where σ denotes a standard deviationof the a_(i) distribution in the tumor. For each lesion, PDFs arecalculated for scans at baseline (e.g. pre-therapy as H_(Pre)(x)) andafter starting therapy (e.g. at week-2 during therapy as H_(2W)(x)).After H_(Pre)(x) is normalized to have an area under the PDF curve equalto one (∫H(x)dx=1), the H_(Pre)(x)s of all the lesions are summed togenerate a pooled PDF (cPDF), in which each lesion has an equalcontribution regardless of its size.

Probabilistic Membership Functions of Projection Coefficients:

Previous studies have suggested that the rCBV (or K^(trans))distribution in a brain tumor is abnormal compared to normal cerebraltissue, as elevated rCBV in a subvolume of the tumor and low rCBV inanother one. A renormalization of tumor vasculature, such as decreasingthe elevated rCBV and increasing the low one, could be an indicator of atumor response to treatment. The DCE-derived physiological parameters(e.g., rCBV and K^(trans)) and projection coefficients, (a₁, a₂ . . . ,a_(T)), are two representations of the DCE curves. Therefore, it isreasonable to assume that a_(i) in the brain metastases could alsodistribute abnormally in contrast to normal tissue, and changes duringtreatment could predict tumor response to therapy. Hence, similar towhat has been done for rCBV previously, the pooled distribution ofH_(pre)(a_(i)) to three classes were classified as high, intermediateand low a_(i) classes using fuzzy-c-means (FCM) clustering analysis byminimizing the objective function J_(m):

J _(m)=Σ_(i=1) ^(N)Σ_(j=1) ^(C) P _(j)(a _(i))^(m) ∥a _(i) −c _(j)∥²,1≦m<∞  (17)

where c_(j) is a prototype vector of the jth class, P_(j)(a_(i)) is aprobabilistic membership of a_(i) value belonging to the jth class, andm is a fuzzy exponent and chosen as 2. The probabilistic membershipfunction, P_(j)(a_(i)), describes that a voxel having a projectioncoefficient a_(i) has a probability P belonging to a class j, which is anew representation of a_(i) value of a tumor voxel (mathematicallytransfers the data from the a_(i) space into a new space).

Projection Coefficient Defined Tumor Subvolume: the primary interest isto test if a change in a subvolume of the tumor defined by high,intermediate or low a_(i) values is related to tumor treatment response.The subvolume (SV) of a tumor was defined with low, intermediate or higha_(i) using the probabilistic membership function Pj(a_(i)) andcalculate a percentage change in the SV from pre-therapy to afterstarting treatment (eg, 2 weeks) as follows:

$\begin{matrix}{{{\hat{\Delta}\; {{SV}_{{{Pre}\rightarrow{2W}},j}\left( \alpha_{i} \right)}} = {\frac{\begin{matrix}{{{GTV}_{2W} \cdot {\int{{{P_{j}\left( \alpha_{i} \right)} \cdot {H_{2W}\left( \alpha_{i} \right)}}{\alpha_{i}}}}} -} \\{{GTV}_{Pre} \cdot {\int{{{P_{j}\left( \alpha_{i} \right)} \cdot {H_{Pre}\left( \alpha_{i} \right)}}{\alpha_{i}}}}}\end{matrix}}{{GTV}_{Pre} \cdot {\int{{{P_{j}\left( \alpha_{i} \right)} \cdot {H_{Pre}\left( \alpha_{i} \right)}}{\alpha_{i}}}}}~ \cdot 100}},{j \in \left\{ {{low},{intermediate},{{or}\mspace{14mu} {high}}} \right\}}} & (18)\end{matrix}$

where GTV denotes gross tumor volume.

Tumor Subvolume Defined by Combined Projection Coefficients: The overallaim of developing a prediction model for a clinical decision supportsystem is to find a combination of factors that accurately anticipate anindividual patient's outcome. Hence, a technique may include combiningdifferent cPCs to improve prediction for tumor response compared tousing one cPC. To do so, first a joint histogram of (a₁, a₂ . . . ,a_(M)) of a lesion is computed, e.g. H(a₁=x1, a₂=x2, . . . a_(M)=xM).Then, a joint probability function, P({α_(i)}, {β_(i)}), is defined asfollows:

$\begin{matrix}{{{P\left( {\left\{ a_{i} \right\},\left\{ \beta_{i} \right\}} \right)} = \frac{{p_{j}\left( a_{1} \right)} + {\sum\limits_{i = 2}^{M}{\beta_{i}{p_{j}\left( a_{i} \right)}}}}{1 + {\sum\limits_{i = z}^{M}\beta_{1}}}},{\beta_{1} = 1}} & (19)\end{matrix}$

where β_(i) is the weighting factor of each coefficient and jε{low,intermediate, or high}. Applying the joint probability function to Eq.(18), a percentage change in a subvolume of a tumor defined by {α_(i)}classes from pre-therapy to after starting treatment (e.g. 2 weeks (2W)) is given by:

$\begin{matrix}{{{\hat{\Delta}\; {{SV}_{{Pre}\rightarrow{2W}}\left( {\left\{ \alpha_{i} \right\},\left\{ \beta_{i} \right\}} \right)}} = {\frac{\begin{matrix}{{{GTV}_{2W} \cdot {\int{\int{- {\int{{P\left( {\left\{ \alpha_{i} \right\},\left\{ \beta_{i} \right\}} \right)}{H_{2W}\left( {\text{?} - \text{?}} \right)}{\text{?}}}}}}}} - {\text{?}} -} \\{{{GTV}_{Pre} \cdot {\int{\int{- {\int{{P\left( {\left\{ \alpha_{i} \right\},\left\{ \beta_{i} \right\}} \right)}{H_{Pre}\left( {\text{?} - \text{?}} \right)}{\text{?}}}}}}}} - {\text{?}}}\end{matrix}}{{{GTV}_{Pre} \cdot {\int{\int{- {\int{{P\left( {\left\{ \alpha_{i} \right\},\left\{ \beta_{i} \right\}} \right)}{H_{pre}\left( {\text{?} - \text{?}} \right)}{\text{?}}}}}}}} - {{\text{?}}{\text{?}}}} \cdot 100}}{\text{?}\text{indicates text missing or illegible when filed}}} & (20)\end{matrix}$

The weighting factor {β_(i)} is selected based upon the best predictionof response from a developmental dataset and evaluated by an independentdata set.

Usage Phase:

for a new patient scan, first, pre-processing of the DCE curves wasperformed and compute the projection coefficient maps of the first M orselected principal components. The histograms or a joint histogram ofthe selected coefficients were computed within the tumor. Then, usingthe probability membership function obtained in the development step,the cPC-defined tumor subvolumes were calculated by Eqs. (19) or (20).Finally, a change of the subvolume from pre-therapy to during or posttherapy is determined.

Evaluation; Endpoint:

a percentage change in the gross tumor volume (GTV) from pre to post RTwas used as an endpoint for response assessment. Several patients didnot have 3 or 6 months post treatment imaging follow-ups. For thepatients in whom 3 and 6 months post-RT images were available, therewere good correlations in the GTV changes between 1 and 3 months post RTand between 3 and 6 months post RT. Also, previous studies indicate thatbrain metastases exhibit little pseudo-response and pseudo-progressionone month after RT. Therefore, a percentage change in the GTV fromPre-RT to 1 month post RT was used, {circumflex over(Δ)}GTV_(Pre→1M Post-RT), as a measure of tumor response to therapy.From Pre-RT to 1M Post-RT, 16 tumors had a decrease in the GTV at least25%, defined as responsive, 11 tumors had an increase at least 25%,defined as progressive, and the remaining 18 were defined as stable. Itwas noticed that there were heterogeneous responses of multiple lesionsfrom a single patient. Thus, each lesion was considered independently.

Parameter Selection and Evaluation:

First, the priority of the candidate parameters and subvolumes wereranked by testing if the changes in ΔSV_(Pre-RT→2W)

(a_(i)) significantly differentiated responsive tumors from combinedstable and progressive ones using Mann-Whitney U Test. Considering Mprincipal components and two independent subvolumes (only two of thethree are independent) for each component, a p-value<(0.05/2M) withBonferroni correction was considered as a significant cutoff to selectthe parameters. The conventional metrics, such as a percentage change inthe GTV from Pre-RT to 2 W ({circumflex over (Δ)}GTV_(Pre→2W)) and achange in the mean rCBV values of a tumor from pre-RT to 2 W({circumflex over (Δ)}μ_(Pre→2W)(rCBV)), were not considered as thecandidate parameters in the model, and thus not used for multiplecomparison justification. Next, univariate analysis was performed toevaluate sensitivity and specificity of the selected significant metricsidentified in the previous test for predicting responsive tumors usingReceiver Operating Characteristic analysis (software package ROCKIT).Also, these newly developed metrics were compared with the conventionalmetrics including a percentage change in the GTV from Pre-RT to 2 W,{circumflex over (Δ)}GTV_(Pre→2W), and a change in the mean rCBV valuesof a tumor from pre-RT to 2 W, {circumflex over (Δ)}μ_(Pre→2W)(rCBV),for predicting post treatment response. The significant difference ofthe area under ROC curves (AUC) between the metrics were compared byt-test, for which the standard error and the difference between the twoAUCs were calculated by the method proposed by DeLong et al. To createthe tumor subvolume defined by combining more than one cPC, the maximumAUC were used to determine the {β_(i)}.

Results; Principal Components:

PCA revealed that the first three principal components (PCs) comprisedmore than 99.99% of the energy of the DCE curves of brain metastases, ofwhich the first component contributed approximately 99.8% while thesecond and third components had 0.08% and 0.02% contributions,respectively, as shown in FIG. 3. Further investigation revealed thatthe first component is related to the area under each DCE curve, whilethe second and third ones were associated with the enhancement rate of aDCE curve and its derivative, respectively. Therefore, the analysisdescribed in the method section was applied to the histograms of thefirst three cPCs in a tumor to determine the subvolume of the tumor witha given class.

TABLE 5 Differences between responsive, stable and progressive brainmetastases using cPC defined tumor subvolume, physiological definedtumor subvolume and conventional metrics Group of lesions Analysis PostAnalysis R vs. R vs. S S vs. P R vs. P Metric 55 S & P} p-valueProjection Coefficient Defined {circumflex over (Δ)}SV_(pre→2W,j)(a₁) j= low 0.5937 0.8766 0.7024 0.3878 Tumor Subvolumes j = intermediate0.0773 0.2477 0.3339 0.0457 j = high 0.0017** 0.0199* 0.1321 0.0015**{circumflex over (Δ)}SV_(pre→2W,j)(a₂) j = low 0.4843 0.7431 0.77020.3359 j = intermediate 0.5774 0.8766 0.2002 0.1596 j = high 0.06610.3979 0.0561 0.0096** {circumflex over (Δ)}SV_(pre→2W,j)(a₃) j = low0.0094** 0.0068** 0.2002 0.1323 j = intermediate 0.4133 0.7693 0.29090.2083 j = high 0.8403 0.1522 0.0143 0.1088 {circumflex over(Δ)}SV_(pre→2W,high,low)(a₂, a₃, 0.3){circumflex over ( )} 0.0005 0.00180.3568 0.0053 Physiological Defined Tumor {circumflex over(Δ)}SV_(pre→2w,high)(rCBV) 0.0057** 0.0338* 0.3568 0.0072** Subvolumes{circumflex over (Δ)}SV_(pre→2W,high)(k^(tranz)) 0.4992 0.6663 0.0162*0.0406* {circumflex over (Δ)}SV_(pre→2W,high,high)(rCBV, k^(tranz), 0.6)0.0015** 0.0199* 0.0687 0.0012** Conventional Metrics {circumflex over(Δ)}μ_(pre→2W)(rCBV) 0.0066** 0.0049** 0.2336 0.1088 {circumflex over(Δ)}μ_(pre→2W)(k^(trans)) 0.8775 0.5233 0.1704 0.5704 {circumflex over(Δ)}GTV_(pre→2W) 0.0124* 0.1086 0.0653 0.0039** Abbreviations: GTV =gross tumor volume; R = responders; S = stables; P = Progressive; cPC =projection Coefficient; {circumflex over ( )}The optimum value of β₃ is0.3, see the results of the ROC analysis. *P < 0.05; **P < 0.01. Thecandidate subvolume for each principle component is highlighted.

Association of the cPC-Defined Tumor Subvolumes with Response:associations of the changes in the first three cPC-defined brainmetastases subvolumes with high, intermediate or low coefficients frompre-RT to 2 W with the tumor response to treatment are given in Table 5.Since the first three components were only compared, a p-value<0.01 wasconsidered as significance to select the candidate parameters. It wasfound that for the responsive group, a percentage decrease in thehigh-a1 subvolumes of the tumors from Pre-RT to 2 W differedsignificantly from the group combining progressive and stable tumors(p<0.0017), Table 5. It was observed a similar but weaker trend for thehigh-a2 subvolume (p<0.07). Furthermore, a percentage decrease in thelow-a3 subvolume of the tumor was associated with tumor response(p<0.01). A percentage decrease in the subvolumes defined by combiningthe high-a1 and low-a3 classes from pre-RT to 2 W revealed that addinga3 improved the statistical significance for differentiating theresponsive tumor from the group of stable and progressive lesionscompared to either coefficient alone but a2 did not add discriminatoryinformation. As a post analysis, the comparisons between other lesiongroups are also given in Table 5.

Predictive Values of the cPC-Defined Tumor Subvolumes:

it was explored that the predictive value of the decrease in thesubvolumes of the brain metastases defined by the cPCs from Pre-RT to 2W for predicting responsive tumors post-RT, and compared theirperformance with the decrease in subvolumes of the tumors defined by thehigh rCBV and high Ktrans and two conventional metrics. The ROC analysisshowed that the AUCs were 0.83±0.06 (±SEM), 0.77±0.07, 0.80±0.07,0.70±0.08, 0.67±0.08 and 0.56±0.09 for {circumflex over(Δ)}SV_(Pre→2W,high)(α₁), {circumflex over (Δ)}SV_(Pre→2W,low)(α₃),{circumflex over (Δ)}SV_(Pre→2W,high)(rCBV), {circumflex over(Δ)}μ_(Pre→2W)(rCBV), {circumflex over (Δ)}GTV_(Pre→2W) and {circumflexover (Δ)}SV_(Pre-2W,high)(K^(trans)), respectively, as shown in FIGS. 4Cand 4D, indicating the high-a1 defined subvolume of the tumor had thebest performance among the tested variables for predicting responsivetumor. The subvolumes defined by the high-a1 and low-a3 classes with theweighting factor=0.3, determined by empirical evaluation of the AUCs asshown in FIG. 4D, resulted in the largest AUC, 0.88±00.5. The subvolumesdefined by the high-rCBV and high-Ktrans classes with the weightingfactor=0.6 resulted in the AUC of 0.86±0.06.

C. Apparent Diffusion Coefficients in Subvolume Analysis

While the techniques discussed above (i.e., the PK modeling techniquesand the PCA techniques) measure the vascular properties of the tumor,other techniques that measure cellularity may be used in conjunctionwith the subvolume analysis of FIG. 10. Cellularity may be determined byobtaining imaging-derived cellularity feature data that measures thewater mobility in the tissue of the tumor via a diffusion weightedmagnetic resonance (DW-MRI). Similar to vascularity subvolume analysisinputs (i.e., the PK modeling technique and PCA technique), as discussedabove, the cellularity features of subvolumes of tumors may be utilizedto predict treatment outcome as well as shown in FIG. 10.

II. Diffusion Abnormality Index Analysis

The DAI technique herein is similar to the subvolume analysis technique,as discussed above, in that it uses the imaging-derived data to predicttumor response to therapy. However, this DAI technique develops a DAI toquantify the extent of abnormality of the tumor ADC histogram comparedto normal tissue. A normal tissue ADC histogram, HNT (ADC), may beobtained in a normal brain volume of 3-4 cc with the peak normalizedto 1. The tumor ADC histogram that usually spreads beyond HNT (ADC) isdivided by HNT (ADC) into 3 categories: low (high cellularity), normal,and high (edema and necrosis) ADC. An abnormal diffusion probabilityfunction (DAProF) of the tumor may be then defined by 1-HNT (ADC) andband-pass filtered to reduce noise influence at the two tails of thehistogram. Given that changes in low and high ADCs could have differentroles in response assessment, a factor (0<α<1) may be used to weight thelow ADC contribution to the DAProF related to high ADC's. As a result,an integral of the DAProF-weighted tumor ADC histogram may be calculatedand used to define the DAI.

In an example, the DAI changes were evaluated to differentiateresponsive, stable and progressive lesions in 24 patients who had brainmetastases and were treated by either whole brain radiation therapy(WBRT, 28 mostly radiosensitive lesions) alone or combined withBortezomib as a radiation sensitizer (39 radioresistant lesions). Theperformance of DAI for predicting the post-treatment radiographicresponse was also evaluated by Receiver Operating Characteristicanalysis and compared with changes in gross tumor volume (GTV) observedduring the same time interval. In lesions treated by WBRT alone, theresponsive tumors showed a greater decrease in DAI at week 2 afterstarting the radiation than stable and progressive lesions (p<0.00004).The performance of DAI worsened for the radioresistant lesions treatedby WBRT combined with Bortezomib but still better than changes in theGTV, suggesting that the physiological change occurs prior to thevolumetric change.

To determine cellularity, the DW-MRI is used and has been shown to be animaging biomarker for assessing tumor aggressiveness and early responseto therapy in various cancers. The DW-MRI acquisition is rapid andnoninvasive, and uses neither exogenous contrast agent nor ionizingradiation. The apparent diffusion coefficient, quantified from DW-MRI,measures water mobility in tissue, and is sensitive to cellular density,extracellular space tortuosity, and intactness of cellular membranes.However, quantification of an ADC change in the tumor is still achallenge and affects sensitivity and specificity of diffusion indicesfor early prediction of tumor response to therapy, mainly because theADCs in a tumor manifest a heterogeneous distribution pattern, due tospatial variation in cellular density, cell structure and water content.In a high cellular region, mobility of water molecules is restricted,and thus the ADC is low; while in a region with necrosis or edema, watermolecules move more freely and hence the ADC is high. Animal studieshave shown that the ADC in a tumor is inversely correlated with thetumor cellularity. When a tumor responds to treatment, the ADC in thehigh cellular region could increase due to cell shrinkage followed byphagocytosis or necrosis. Also, the ADC in the edema region coulddecrease due to drainage of water. Thus, the direction of the changedepends on the measurement location of the ADC and the original value ofthe ADC. Therefore, the heterogeneity in the tumor ADCs and the complexchanges that occur after treatment suggest that a change in the mean ADCof a tumor may be a poor indicator for therapy response.

Thus far, several methodologies have been proposed to quantify the ADCchanges in tumors, beyond a change in the mean ADC of a tumor, forresponse assessment. The functional diffusion map (fDM), probably themost common approach, measures the voxel-to-voxel interval changes in apair of the co-registered ADC images acquired pre and post the start oftherapy. The voxels with an ADC change above a threshold are consideredas a measure of response. Despite the promising results of the fDM-basedapproach and its modifications, again, the issue of voxel-to-voxelmisregistration, particularly in the region where a tumor volume shrinksor grows during the interval of measurements, is problematic. Also,since the decrease/increase in regions of high cellularity or edemacould have different interpretations, it is important to consider theinitial ADC values to interpret subsequent changes correctly.Alternatively, analysis of the tumor ADC histogram has been proposed. Abi-normal distribution mixture model has shown that the mean value ofthe low-ADC distribution can predict therapy response in gliomas. Also,changes in mean, skewness and kurtosis of the ADC histogram or theminimum value of the ADC in tumors have been related to survival and thetreatment outcome. However, these methods have not considered changes inthe whole ADC histogram, including both regions with high cellularityand edema, in each of which a change could reflect a part of the processof a tumor response to therapy and therefore may lead to losinginformation from the analyses. Thus, it is highly desirable to develop anew methodology for quantifying the tumor ADCs to improve theperformance of DW-MRI for therapy assessment.

Experiment 4

The techniques herein use the DAI to quantify the extent of diffusionabnormality of brain metastases for early prediction of tumor responseto therapy. For instance, brain metastases, which are the most prevalentform of intracranial cancer, exceed the number of primary brain tumorsby at least ten times and occur in approximately 25% of all cancerpatients. In a DAI-based approach, an abnormal diffusion probability wasassigned to each voxel of the tumor based upon its ADC value relating tothe normal tissue ADC distribution. Then, a DAI of the tumor wasobtained by summing all the abnormal probabilities within the lesion. Itwas tested whether an early change in the DAI could predict response ofbrain metastases in the patients who were treated by either whole brainradiation therapy alone or in combination with Bortezomib as a radiationsensitizer.

Histogram of ADCs in a Tumor:

to analyze the ADC distribution in a tumor and a subsequent changeduring treatment, a histogram of ADCs in a lesion measured at each timepoint was generated with 150 evenly-spaced bins that cover the ADCs ofall lesions of interest. The ADC histogram, H(ADC=x), of a lesion iscalculated as:

H(ADC=x)≡n _(i) : x−ε≦ADC _(i) ≦x+ε  (21)

where n_(i) is the number of voxels within |ADC_(i)−x|<ε, and

${ɛ = \frac{\sigma}{4}},$

a smooth factor of H, where σ is a standard deviation of the ADCdistribution in the tumor. Then, the ADC histogram of each lesion ateach scan is normalized to have an area under the histogram equal to one(∫H(x)=1). The ADC histogram of normal tissue (H_(NT)(ADC)) iscalculated in a similar fashion except the peak is normalized to one.FIG. 11A depicts examples of (i) ADC histograms in a region of normalwhite matter (solid line), and (ii) a progressive brain metastasis priorto treatment (dotted line) and 2 weeks after starting the treatment(dashed line). As shown in FIG. 11A, the ADC histogram of a tumorspreads widely, and is skewed and/or shifted when compared to thenormally distributed ADC histogram of normal tissue. On the other hand,FIG. 11B depicts the DAProF of the same tumor (solid lines) in which theDAProF is equal to (1-H_(NT)(ADC)) filtered by Kaiser band-pass filterand weighted by a factor α (<1) for high ADCs.

Diffusion Abnormality Probability Function:

next, a diffusion abnormality probability function (DAProF) wasdeveloped to characterize the whole tumor ADC histogram based upon thenormal tissue ADC distribution of each patient. The HNT(ADC) divides thetumor ADC histogram into three segments with low, normal, and high ADCs(FIG. 11B). The first and latter are related to high cellular densityand edema, respectively. Therefore, for each patient, a DAProF can bedefined as 1−HNT(ADC), and filtered by Kaiser band-pass filter (BPF)centered at the peak of HNT(ADC) to reduce noise influence in thecomputation at two tails where the ADC approaches to positive ornegative infinite (or zero) as:

DAProF=BPF·(1−H _(NT)(ADC))  (22)

Eq. (22) denotes the tumor ADCs are abnormal except in the areas wherethe ADC values are in the range of normal tissue ones. In Eq. (22), 90%of confidence interval of HNT(ADC) is used to define the band-width ofBPF. Because that changes in the low-ADC (high cellularity) tumor regioncould be associated with therapy response differently, a weightingfactor α (<1) is used to weight low and high ADC contributions unequallyin the DAProFα as:

$\begin{matrix}{{DAProF}_{\alpha} = \left\{ \begin{matrix}{{DAProF}_{Low} = {{BPF} \cdot \left( {1 - {H_{NT}({ADC})}} \right)}} & {{ADC} \leq {ADC}_{Norm}} \\{{DAProF}_{High} = {\alpha \cdot {BPF} \cdot \left( {1 - {H_{NT}({ADC})}} \right)}} & {{ADC} > {ADC}_{Norm}}\end{matrix} \right.} & (23)\end{matrix}$

where ADC_(norm) is the ADC at the peak of the normal tissuchistogram.Finally, the DAProF_(α) is normalized to one at the peak. Note thatDAProF_(α) is patient-specific.

Diffusion Abnormality Index:

to quantify the extent of diffusion abnormality in a tumor at a specificscan time (r), DAI is defined as:

DALI_(α)(τ)=GTV_(τ) ·∫H _(τ)(x)·DAProF_(α)(x)dx  (24)

where GTV and H (x) denote the gross tumor volume and the normalizedtumor ADC histogram at time τ, respectively. As a result, the DAI is asummation of diffusion abnormality from all voxels of a tumor. It isworthwhile to note that the DAI is minimum for normal tissue. A low orhigh ADC abnormality index can also be obtained by replacing DAProF byDAProF_(Low) or DAProF_(High) in Eq. (24). Finally, a change in the DAIfrom pre-RT to 2 W is calculated as:

$\begin{matrix}{{\hat{\Delta}\; {DAI}_{{\alpha \; {pre}}\rightarrow{2W}}} = {\frac{{{DAI}_{\alpha}\left( {2W} \right)} - {{DAI}_{\alpha}\left( {{pre} - {RT}} \right)}}{{DAI}_{\alpha}\left( {{pre} - {RT}} \right)} \cdot 100.}} & (25)\end{matrix}$

In response to therapy, the ADCs in the region with high cellularity mayincrease due to cell shrinkage or necrosis, and in the region of edemamay decrease due drainage of water into tumor cells. However, Eq. (25)combines the two contributions into a single metric for assessing tumorresponse to a specific therapy regimen.

Patient Sample:

twenty four patients who had brain metastases and were treated by WBRTwere enrolled in an institutional review board (IRB)-approvedprospective MRI study (12 women and 12 men, ages 40-76 years, Table 6).The histology included melanoma (14), non-small cell lung cancer (6),renal cell carcinoma (1), breast cancer (2), and head & neck squamouscell carcinoma (1). All patients received WBRT with a total dose of 30Gy (16 patients) or 37.5 Gy (8 patients). Thirteen patients, withradioresistant metastases, also received Bortezomib during WBRT as aradiation sensitizer. Each lesion was analyzed individually due tointra-patient heterogeneous lesion response to therapy. If a patient hadthree metastases or fewer, all lesions were included. If a patient hadmore than three lesions, only the three largest lesions were analyzed.If a patient had more than three lesions larger than 0.5 cm3, alllesions larger than 0.5 cm3 were included. As a result, a total of 67metastatic lesions were included in the dataset, of which 28 weretreated with radiation therapy alone and the remaining lesions weretreated with radiation therapy in combination with Bortezomib as aradiation sensitizer.

TABLE 6 Patient characteristics information Patient characteristicsinformation Total Tumor accumulated Concurrent No. Volume Pt. GenderAge(Y) Histology dose/Fx (Gy) Therapy of L. Range (cm³) 1 Female 54Breast Cancer 37.5/2.5 None 6  0.5-11.78 2 Female 60 NSC Lung Cancer37.5/2.5 None 1 0.52 3 Male 52 NSC Lung Cancer 30/3 None 1  0.479 4Female 64 NSC Lung Cancer 37.5/2.5 None 1 0.11 5 Male 43 Head & Neck SCC30/3 None 1 0.60 6 Male 58 NSC Lung Cancer 30/3 None 3  2.38-10.69 7Female 66 NSC Lung Cancer 37.5/2.5 None 1 0.95 8 Female 53 Breast Cancer30/3 None 4 0.45-8.9  9 Male 40 Melanoma 37.5/2.5 None 3 0.06-1.23 10Male 58 Melanoma 30/3 None 4 0.48-6.47 11 Female 51 NSC Lung Cancer 30/3None 3 0.50-4.55 12 Male 63 Renal Cell 30/3 Bortezomib 2 13.23-14.67Carcinoma 13 Male 41 Melanoma 37.5/2.5 Bortezomib 3 0.15-1.24 14 Female52 Melanoma 37.5/2.5 Bortezomib 1 2.74 15 Female 45 Melanoma 30/3Bortezomib 1 2.07 16 Male 49 Melanoma 30/3 Bortezomib 2 0.17-4.09 17Male 61 Melanoma 37.5/2.5 Bortezomib 7  0.5-17.67 18 Female 55 Melanoma30/3 Bortezomib 2 0.42-0.55 19 Male 76 Melanoma 30/3 Bortezomib 1 0.6820 Female 46 Melanoma 30/3 Bortezomib 8  0.5-1.95 21 Female 57 Melanoma30/3 Bortezomib 2 0.94-1.58 22 Male 60 Melanoma 30/3 Bortezomib 30.18-1.31 23 Female 74 Melanoma 30/3 Bortezomib 4 0.69-5.81 24 Male 67Melanoma 30/3 Bortezomib 3  0.62-11.10 Abbreviation: Pt. = patient; Y =year; NSC = non-small cell; SCC = squamous cell carcinoma; No. of L. =number of lesions; and Fx = fraction size.

Image Acquisition and Pre-Processing:

all patients had MRI scans on a Philips 3T scanner prior to radiationtherapy (Pre-RT), 2 weeks after the start of RT (2 W), and 1 month afterthe completion of treatment (1M Post-RT). MRI scans included pre andpost Gd-DTPA volumetric T1-weighted images, 2D T2-weighted images, anddiffusion-sensitive images. The diffusion weighted images were acquiredusing a spin-echo echo-planar imaging sequence (TR/TE=2636/46 msec) withb0=0, and diffusion weighting along three orthogonal directions andb1=1,000 sec/mm2 to calculate the ADC images.

Using an in-house software package, all ADC images were co-registered topre-RT post-Gd T1-weighted images by rigid transformation and mutualinformation to have a voxel size of 0.94×0.94×3 (mm3). After each lesionof interest was contoured on the post-Gd T1 weighed images obtainedpre-RT, 2 W and 1M post-RT by a physician, the tumor volumes weretransferred onto the ADC maps obtained at the same time point. For eachpatient, a volume of 3-4 cc of normal white matter or cerebellum tissue,depending upon the location of the tumor of interest, was contoured onthe pre-RT post-Gd T1-weighed images and transferred onto the pre-RT ADCmap to obtain a distribution of normal ADCs.

DAI for Prediction of Response:

given that previous studies indicate that brain metastases exhibitlittle pseudo-response and pseudo-progression one month after RT, apercentage change in the gross tumor volume (GTV) from Pre-RT to 1 monthpost RT, {circumflex over (Δ)}GTV_(Pre→1M post), was used as a measureof tumor response to therapy. Data was used to verify that there waslittle pseudo-response or pseudo-progression in brain metastases treatedby either WBRT alone or WBRT with Bortezomib. From Pre-RT to 1M Post-RT,of the 27 lesions treated with radiation therapy alone, 14 had adecrease in the GTV of at least 25%, defined as responsive, 7 had anincrease of at least 25%, defined as progressive, and the remaining 6were defined as stable. For the 39 lesions treated by radiation therapyin combination with Bortezomib, 10 lesions were responsive, 13 lesionswere progressive and 17 lesions were stable.

Predictive Model:

α(<1) in {tilde over (Δ)}DAi_(α,pre-RT→2W) was optimized in eachtreatment group by maximizing the group difference between theresponsive and progressive lesions using Mann-Whitney U Test. Using theoptimal value of α, it was further tested if could differentiateresponsive from stable tumors, and stable from progressive tumors inboth treatment groups. A p-value<0.05 was considered as significant.Next, sensitivity and specificity of {circumflex over (Δ)}DAi_(αpre→2W)for predicting non-responsive tumors was tested, including bothprogressive and stable tumors by Receiver Operating Characteristic (ROC)analysis (software package ROCKIT). Also, the performance of {circumflexover (Δ)}DAI for predicting post-treatment response was compared withthe ADC metrics previously published by others, such as a mean of thelow ADC distribution from the bi-normal ADC distribution mixture model,and skewness and kurtosis of tumor ADC histograms, and conventionalmetrics, such as a percentage change in the GTV, pre-treatment minimumADC, a minimum ADC change and a change in the mean of tumor ADCs frompre-RT to 2 W. The significant difference of the area under ROC curves(AUC) between the metrics were compared by t-test, for which thestandard errors and the difference between the two AUCs were calculatedby the method proposed by DeLong et al. The leave-one-out technique wasused to measure the prediction risk of {circumflex over (Δ)}DAI.Considering that melanoma metastasis is more radiation therapy resistantthan the metastases from NSC Lung and breast cancers, the two patientswith melanoma metastases from the analysis of the first treatment groupwere excluded to verify whether including melanoma metastases in thisgroup alters the results.

Association of DAI with Response:

the best separation of the group difference between the responsive andprogressive tumors resulted in α values of 0.7 and 0.2 for lesionstreated with radiation therapy alone and in combination with Bortezomibas a radiation sensitizer, respectively, suggesting that decreases inabnormality associated with high-ADCs (edema/necrosis) may havedifferent roles in response assessment, depending on the treatmentregimen and tumor type.

For lesions treated with radiation therapy alone, as anticipated, theDAI showed a significantly greater decrease from Pre-RT to 2 W in theresponsive tumors than the progressive ones (p<0.0003), but also thestable lesions (p<0.0035) or the non-responsive tumors (including bothprogressive and stable ones) (p<0.00004) revealed a decrease as well, asshown in Table 7. For the volumetric change observed during the sameperiod, the percentage decrease in the GTV in the responsive groupdiffered significantly from the stable group (p<0.03), the progressivegroup (p<0.0004), and the group of combining the progressive and stabletumors (p<0.0003). For the metrics that are often found in literature,their performances for differentiation of responsive, stable andprogressive lesions are summarized in Table 7. As seen, skewness wasable to differentiate between the responsive and progressive lesions,but worse than the percentage changes in the GTV and DAI for theradiation therapy-alone group. The mean of the low ADC distributiondetermined from the bi-normal Gaussian mixture model could notdifferentiate the responsive tumors from progressive or stable ones.

For the radiation resistant lesions treated with radiation therapycombined with Bortezomib, the DAI change from Pre-RT to 2 W was able todifferentiate between the responsive and progressive lesions (p<0.01),but not between the responsive and stable lesions (see Table 8). Othercommonly used metrics could not differentiate any groups. Finally, FIG.2. shows the box plots of the significant metrics (ΔDAI, ΔGTV,ΔSkewness, and ΔKurtosis) listed in Tables 7 and 8 for responsive,stable and progressive lesions treated with radiation therapy alone orin combination with Bortezomib.

TABLE 7 Association of the different diffusion metrics with response inlesions treated by whole brain radiation therapy alone. Lesion ResponseGroups R vs. S S vs. P R vs. P R vs. {S & P} Metric (Pre-RT->Week2)p-value ΔDAI_(0.7) 0.003 0.004 0.0003 0.00004 ΔGTV 0.02 0.004 0.00040.0003 ΔSkewness 0.29 0.44 0.02 0.03 ΔKurtosis 0.41 0.73 0.06 0.08 Δμ0.29 0.62 0.57 0.31 ΔMinADC 0.33 0.13 0.29 0.92 MinADC_(preRT) 0.55 0.230.77 0.89 Δlow-ADC (BNGM) 0.03 0.23 0.62 0.31 Low-ADC_(preRT) (BNGM)0.09 0.03 0.35 0.21 Δ = Change from Pre-RT to Week 2; μ = mean of tumorADC; Min = minimum; Low-ADC (BNGM) = the mean of the low ADCdistribution in the bi-normal Gaussian mixture model; GTV = gross tumorvolume; DAI = diffusion abnormality index; R = responsive; S = stable; P= progressive.

Table 7 illustrates the group differences between responsive, stable,and progressive tumors treated with radiation therapy. For each metric,the absolute or percentage change from pre-RT to week 2 were evaluatedand the best performance is reported. The black rows show significantmetrics.

TABLE 8 Association of the different diffusion metrics with response inradioresistant lesions treated by whole brain radiation therapy incombination with Bortezomib as a radiation sensitizer. Lesion ResponseGroups R vs. S S vs. P R vs. P R vs. {S & P} Metric (Pre-RT->Week2)p-value ΔDAI_(0.2) 0.25 0.03 0.01 0.04 ΔGTV 0.93 0.07 0.16 0.50ΔSkewness 0.47 0.98 0.43 0.39 ΔKurtosis 0.89 0.77 0.73 0.78 Δμ 0.47 0.870.87 0.59 ΔMin 0.13 0.52 0.27 0.13 Min_(preRT) 0.58 0.67 0.68 0.57ΔLow-ADC (BNGM) 0.65 0.67 0.78 0.66 Low-ADC_(preRT) (BNGM) 0.58 0.940.82 0.68 Δ = Change from Pre-RT to Week 2; μ = mean of tumor ADC; Min =minimum; Low-ADC (BNGM) = the mean of the low ADC distribution in thebi-normal Gaussian mixture model; GTV = gross tumor volume; DAI =diffusion abnormality index; R = responsive; S = stable; P =progressive.

Table 8 illustrates the group differences between responsive, stable andprogressive tumors treated with radiation therapy. For each metric, theabsolute or percentage change from pre-RT to week 2 were evaluated andthe best performance is reported. The black rows show significantmetrics.

FIG. 12 depicts box plots of ΔDAI (A and E), ΔGTV (B and F), ΔSkewness(C and G) and ΔKurtosis (D and H) for responsive, stable and progressivelesions treated by WBRT alone (top) or in combination with Bortezomib asa radiation sensitizer (bottom). The top row of FIG. 12 shows that ΔGTVand ΔDAI0.7 differentiate the responsive lesions from the stable andprogressive ones treated by WBRT while ΔDAI0.2 differentiate theresponsive and stable lesions from the progressive ones when Bortezomibis used as a radiation sensitizer.

Performance of the DAI for Prediction of Response:

the performance of the significant or marginally significant metrics inTables 7 and 8 was evaluated, such as ΔDAI, ΔGTV, ΔSkewness, andΔKurtosis, for prediction of non-responsive tumors post-RT for bothtreatments. When lesions treated with radiation therapy alone, the ROCanalysis showed that the AUC was 0.96±0.04 (±SEM), 0.91±0.06, 0.64±0.1,and 0.71±0.1 for ΔDAI_(0.7), ΔGTV, ΔSkewness, and ΔKurtosis,respectively, as shown in FIG. 13A. A pair-wise comparison of the ROCcurves of the two metrics revealed that ΔDAI0.7 is a better predictor,but not significantly, than ΔGTV (p<0.15). For 92% sensitivity, ΔDAI0.7and ΔGTV achieved 87% and 73% specificity, respectively. When Bortezomibwas used as a radiation sensitizer, the AUCs of 0.70±0.09 (±SEM),0.57±0.1, 0.61±0.1, and 0.52±0.1 were achieved for ΔDAI_(0.2), ΔGTV,ΔSkewness, and ΔKurtosis, respectively, as shown in FIG. 13B. Inaddition, the leave-one-out analysis resulted in α=0.7±0.0 (SEM) for thelesions treated by radiation therapy alone, and α=0.19±0.02 for thetumors treated by combining radiation therapy with Bortezomib,indicating that there is no significant bias in the α value selectionand also that α is a treatment-specific and even disease-specificparameter in the DAI. Finally, exclusion of melanoma metastases from thefirst treatment group resulted in an AUC of 0.92±0.05, indicatingincluding or excluding the radiation resistant lesions from the firsttreatment group did not alter the results.

DAProF Map:

examples of maps of the ADC and the diffusion abnormality probabilityfunction, DAProF, for a responsive lesion, treated with radiationtherapy alone, and a progressive one, treated with radiation therapy incombination with Bortezomib, at Pre-RT and 2 W are shown in FIG. 14. Inparticular, FIG. 14 depicts T1-weighted images at Pre-RT and 2 W (toprows), ADC maps at Pre-RT and 2 W (middle rows), and maps of diffusionabnormality probability functions at Pre-RT and 2 W (bottom rows) for aresponsive and a progressive lesion. The images of a responsive case(with a volume of 3.7 cc) are depicted in two left columns of FIG. 14and the images of a progressive one (with a volume of 4.1 cc) arerepresented in two right columns of FIG. 14. From Pre-RT to 2 W, the DAIdecreased ˜31% for the responsive lesion, and increased ˜16% for theprogressive one. For the responsive lesion, treated by radiation therapyalone, α=0.7 but for the progressive lesion where Bortezomib was usedα=0.2. From Pre-RT to 2 W, the DAI decreased ˜31% for the responsivelesion but increased ˜75% for the progressive one. For the responsivelesion, the low and high ADC components in the DAIS decreasedapproximately 65% and 21%, respectively. For the progressive lesion, thelow ADC component in the DAI increased 411% but the high ADC onedecreased 20%.

FIG. 15 illustrates an example implementation of the DAI techniquesherein that depicts a flow diagram illustrating an exemplary method forperforming an early assessment of a tumor response to radiation therapy.The method 1500 begins with the system obtaining diffusionweighted-images from a tumor or disease and generating ADCs from theimages (block 1502). The system then generates a histogram from thegenerated ADCs of the tumor (block 1504) and also creates a diffusionabnormality probability function from a ADC histogram of normal tissue(block 1506). The system further creates of diffusion abnormalityprobability map of the tumor or disease as a visual output or target forfocal therapy (block 1508) and generates a DAI from the ADCs of thetumor or disease for diagnosis, prognosis or prediction of treatment(block 1510).

This development of a DAI based upon diffusion weighted magneticresonance imaging for early assessment of brain metastasis response toradiation therapy uses the physiology of abnormal ADCs in a tumor,including both high cellularity and edema. The DAI weights the abnormalADC contributions from high cellularity and edema differently forpredication of therapy response. The performance of DAI in patients whohad brain metastases were evaluated and were treated by either WBRTalone (mostly radiation sensitive lesions) or in combination withBortezomib as a radiation sensitizer (radiation resistant lesions).Compared to other ADC metric published previously and conventionalmetrics, the DAI performed better in predicting volumetric response ofbrain metastases to radiation therapy. Also, the results indicate thatthe diffusion-related physiological change in the tumor occurs earlierthan the morphological change in response to radiation therapy. The DAImay be applied to other tumor types and treatment regimens afterrecalibration, e.g., glioblastoma and head and neck cancers, andanti-angiogenesis therapy. The DAI may be used as a robust imagingbiomarker for early assessment of tumor response and outcome.

In the development of the DAI, the weighting factor, α, was found to bedifferent for different treatments and/or tumor types. For instance,when brain metastases were treated with WBRT alone, the changes inabnormality associating with high-ADCs contributed substantially toresponse assessment. In this case, both a decrease in cellularity and adecrease in edema could indicate treatment response. However, in thetreatment of melanoma metastases to the brain, it was realized that adecrease in abnormality associated with high-ADCs is less important forresponse assessment. Also, the performance of all tested ADC metrics,including the DAI, was found to be worse for patients with melanomametastases than for the metastases that were not from melanoma. Thiscould be due to the nature of melanoma, high vascularity, edema andhemorrhage, or, the effect of Bortezomib on the lesion. BecauseBortezomib could alter vascular properties of the tumor, a change invascular characteristics of the tumor may be an important part of tumorresponse to therapy, and hence a perfusion change could be added intothe DAI to improve the response assessment. The findings also suggestthat the DAI could be used to assess a specific treatment effect on aspecific tumor. For an anti-angiogenesis treatment to brain tumor, e.g.,glioblastoma, a high ADC abnormality reduction indicates the treatmenteffect on the abnormal leaky vasculature but a low ADC abnormalitydecrease suggest the effect on the tumor.

The DAI may have several advantages in comparison with functionaldiffusion map, the most common approach in study of the ADC map. The DAIneed not rely on voxel-to-voxel image registration accuracy, which thefDM-based analysis solely depends upon. Hence, anatomical alteration ofa tumor after starting therapy, e.g., a change in edema, or surgicalcavity, and/or tumor growth or shrinkage, does not have an adverseeffect on the DAI. In addition, the tumor volume is incorporated intothe DAI, and thus a change in DAI represents both physiological andmorphological changes in a tumor, which may increase the sensitivity ofthe DAI for tumor response to therapy. Furthermore, the fDM-basedanalysis only considers an absolute change in the ADC, regardless of theorigin of the ADC, whereas an increase or a decrease in the low or highADC region has a very different underlying implication. Further, an ADCincrease in the region with abnormal low diffusion and a decrease in theregion with abnormal high diffusion both are positive indications for atumor response to therapy. Also, it is important to point out thatalthough a change in the DAI of a tumor does not depend upon voxel-levelaccuracy of registration of images acquired pre and after the start oftherapy, spatial information of diffusion abnormality of a tumor isavailable at any given measurement, as shown in FIG. 14, which could beused for visualization or provide guidance for intensified treatment.

The DAI may offer several advantages in comparison with otherhistogram-based approaches. In some of these techniques, only thelow-ADC portion of the tumor histogram is used for therapy assessment;yet, a reduction in the abnormal high ADC in the edema region may bealso an important indicator for response prediction. Thus, combining thechanges in both abnormal low and high ADC regions has the potential toproduce a better predictor for assessing response to various treatments.The indices based upon the skewness and kurtosis of the tumor ADChistogram neglect diffusion physiology in a tumor and may not be able tocapture the complex change patterns in a heterogeneous tumor forresponse assessment.

The DAI may be used as a biomarker for assessment of brain metastasisresponse to WBRT. Currently, WBRT and stereotactic radio-surgery (SRS)are two routine treatments for brain metastases and prolonging patientsurvival. However, a recent study has shown that WBRT produces adecrease in neurocognitive status compared to SRS. Therefore, more andmore patients are receiving focal treatment for brain metastases. AsWBRT is being done less, more patients are developing new lesions aftertreatment of the initial lesions, and thus are being treated to newlesions over time. Also, the DAI could be extended to other tumor types,e.g., glioblastoma, for early assessment of tumor response to therapy.

The techniques disclosed herein may combine the different properties ofthe subvolumes of a tumor, such as the vascularity of subvolumes of thetumor, the cellularity of subvolumes of the tumor, etc. to calculate amore precise prediction in treatment outcome. For example, the methodmay utilize vascular properties, such as blood flow, blood volume, etc.in predicting one treatment outcome and also may examine the cellularityproperties (i.e., the water mobility in tissue) to determine an abnormaldiffusion coefficient in predicting treatment outcome. The predictionresults from the both properties may be combined together to provide amore refined, accurate overall prediction for treatment outcome.

In the example embodiment illustrated in FIG. 16, a computer system forperforming subvolume analysis of a target tissue is provides. Thetechniques described herein (e.g., in FIGS. 10, 15) may be coded, insoftware, hardware, firmware, or combination thereof, for execution on acomputing device such as that illustrated in FIG. 16. Generally, FIG. 16illustrates an example of a suitable computing system environment 10 tointerface with a medical professional or other user to analyze medicalimaging data. It should be noted that the computing system environment10 is only one example of a suitable computing environment and is notintended to suggest any limitation as to the scope of use orfunctionality of the method and apparatus of the claims.

With reference to FIG. 16, an exemplary system for implementing theblocks of the claimed methods and apparatuses includes a general-purposecomputing device in the form of a computer 12. Components of computer 12may include, but are not limited to, a processing unit 14 and a systemmemory 16. The computer 12 may operate in a networked environment usinglogical connections to one or more remote computers, such as remotecomputers 70-1, 70-2, . . . 70-n, via a local area network (LAN) 72and/or a wide area network (WAN) 73 via a modem or other networkinterface 75. These remote computers 70 may include other computers likecomputer 12, but in some examples, these remote computers 70 include oneor more of a (i) an MRI imaging system, (ii) a CT imaging system, (iii)a PET imaging system, and (iv) a medical records database systems.

Computer 12 typically includes a variety of computer readable media thatmay be any available media that may be accessed by computer 12 andincludes both volatile and nonvolatile media, removable andnon-removable media. The system memory 16 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) and random access memory (RAM). The ROM may include abasic input/output system (BIOS). RAM typically contains data and/orprogram modules that include operating system 20, application programs22, other program modules 24, and program data 26. The computer 12 mayalso include other removable/non-removable, volatile/nonvolatilecomputer storage media such as a hard disk drive, a magnetic disk drivethat reads from or writes to a magnetic disk, and an optical disk drivethat reads from or writes to an optical disk.

A user may enter commands and information into the computer 12 throughinput devices such as a keyboard 30 and pointing device 32, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notillustrated) may include a microphone, joystick, game pad, satellitedish, scanner, or the like. These and other input devices are oftenconnected to the processing unit 14 through a user input interface 35that is coupled to a system bus, but may be connected by other interfaceand bus structures, such as a parallel port, game port or a universalserial bus (USB). A monitor 40 or other type of display device may alsobe connected to the processor 14 via an interface, such as a videointerface 42. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 50 and printer 52,which may be connected through an output peripheral interface 55.

Generally, the techniques herein may be coded any computing language forexecution on computer 12. Image data may be obtained from the remotecomputers 70-1, 70-2, . . . 70-n and stored loaded on to any of thecomputer storage devices of computer 12. Once the image data, includingimage segments, is obtained, a user may input or select the conditionparameters through an input mechanism as described. Although, in otherexamples, the condition parameters may be pre-selected or automaticallydetermined, for example, based on a particular type of analysis that isto be performed. The output of the executable program may be displayedon a display (e.g., a monitor 40), sent to a printer 52, stored forlater use by the computer 12, or offloaded to another system, such asone of the remote computers 70. The output may be in the form of a graphor table indicating the explanation audit. Operations of the system maybe recorded in an log database for future reference as shown. This logdatabase may be accessed at subsequent times when a post-RT image is tobe obtained, for example. In any event, a subvolume processing engineimplementing the processes of FIGS. 10, 15 is implemented on thecomputer 10, in the illustrated example.

More generally, the various blocks, operations, and techniques describedabove may be implemented in hardware, firmware, software, or anycombination of hardware, firmware, and/or software. When implemented inhardware, some or all of the blocks, operations, techniques, etc. may beimplemented in, for example, a custom integrated circuit (IC), anapplication specific integrated circuit (ASIC), a field programmablelogic array (FPGA), a programmable logic array (PLA), etc.

When implemented in software, the software may be stored in any computerreadable memory such as on a magnetic disk, an optical disk, or otherstorage medium, in a RAM or ROM or flash memory of a computer,processor, hard disk drive, optical disk drive, tape drive, etc.Likewise, the software may be delivered to a user or a system via anyknown or desired delivery method including, for example, on a computerreadable disk or other transportable computer storage mechanism or viacommunication media. Communication media typically embodies computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism. The term “modulated data signal” means a signal that has oneor more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency, infrared and other wireless media. Thus, the software may bedelivered to a user or a system via a communication channel such as atelephone line, a DSL line, a cable television line, a wirelesscommunication channel, the Internet, etc. (which are viewed as being thesame as or interchangeable with providing such software via atransportable storage medium).

Moreover, while the present invention has been described with referenceto specific examples, which are intended to be illustrative only and notto be limiting of the invention, it will be apparent to those ofordinary skill in the art that changes, additions and/or deletions maybe made to the disclosed embodiments without departing from the spiritand scope of the invention.

Thus, although certain apparatus constructed in accordance with theteachings of the invention have been described herein, the scope ofcoverage of this patent is not limited thereto. On the contrary, thispatent covers all embodiments of the teachings of the invention fairlyfalling within the scope of the appended claims either literally orunder the doctrine of equivalents.

What is claimed is:
 1. A method of analyzing medical image data of aregion of interest in a sample tissue, the method comprising: obtaining,at a computer system, the medical image data of the region of interest,the medical image data containing image segments; identifying, at thecomputer system, one or more candidate physiological, metabolic,molecular and/or biologic parameters that may indicate an abnormal ordisease phenotype condition within the region of interest; analyzing, ina subvolume analysis engine, each of the image segments using analgorithm to determine, for each image segment, a probability functionfor each of the identified one or more candidate physiological,metabolic, and/or biologic parameters; and modeling the image segments,in the subvolume analysis engine, and analyzing the resulting model toidentify the abnormal or disease phenotype condition within the regionof interest, where the identification produces a diagnosticdetermination of the region of interest, a prognostic determination ofthe target tissue, or a predictive determination of the target tissue.2. The method of claim 1, further comprising automatically applying thediagnostic determination, prognostic determination, or predictivedetermination to a target tissue treatment.
 3. The method of claim 2,wherein the target tissue is a tumor and wherein the target tissuetreatment is radiation therapy.
 4. The method of claim 1, wherein thealgorithm is a fuzzy logic algorithm, a genetic algorithm or Gaussianmixture model.
 5. The method of claim 1, wherein the image segments arevoxels or groups of voxels.
 6. The method of claim 1, wherein the one ormore physiological, metabolic, molecular and/or biologic parametersinclude but not limit to regional cerebral blood volume (rCBV), a volumetransfer coefficient (K^(trans)), diffusion coefficient and/orfluorodeoxyglucose (FDG) uptake.
 7. The method of claim 1, wherein themedical image data is magnetic resonance image (MRI) data, positronemission tomography (PET) image data or single photon emission computedtomography (SPECT) image data.
 8. The method of claim 1, whereinanalyzing each of the image segments comprises applying a probabilitydensity function for each of the candidate parameters for each imagesegment.
 9. The method of claim 8, further comprising determining theprobability density function for each of the candidate parameters for animage segment collected prior to treatment of the target tissue and foran image segment collected after starting treatment of the targettissue.
 10. The method of claim 8, wherein the analysis comprisesmodeling the probability density functions using a fuzzy-c-means (FCM)clustering analysis, Gaussian mixture model and/or other discriminationanalyses.
 11. The method of claim 1, further comprising assigning eachof the image segments to one of the more of the candidate parametersbased on the analysis of the resulting model.
 12. Apparatus foranalyzing medical image data of a region of interest in a sample tissue,the apparatus comprising: a computer system having a processor executinginstructions that, when executed, (i) obtain the medical image data ofthe region of interest, the medical image data containing imagesegments, and (ii) identify one or more candidate physiological,metabolic, molecular and/or biologic parameters or features that mayindicate an abnormal or disease phenotype condition within the region ofinterest; and the computer system further comprising a subvolumeanalysis engine to (i) analyze each of the image segments using analgorithm to determine, for each image segment, a probability functionfor each of the identified one or more candidate physiological,metabolic, and/or biologic parameters, (ii) model the image segments, inthe subvolume analysis engine, and (iii) analyze the resultingmodel/metric to identify the abnormal or disease phenotype conditionwithin the region of interest, where the identification produces adiagnostic determination of the region of interest, a prognosticdetermination of the target tissue, or a predictive determination of thetarget tissue.
 13. The apparatus of claim 12, wherein the subvolumeanalysis engine is configured to further automatically apply thediagnostic determination, prognostic determination, or predictivedetermination to a target tissue treatment.
 14. The apparatus of claim12, wherein the algorithm is a fuzzy logic algorithm, a geneticalgorithm or Gaussian mixture model.
 15. The apparatus of claim 12,wherein the image segments are voxels or groups of voxels.
 16. Theapparatus of claim 12, wherein the one or more physiological, metabolic,molecular and/or biologic parameters include but not limit to regionalcerebral blood volume (rCBV), a volume transfer coefficient (K^(trans)),diffusion coefficient and/or fluorodeoxyglucose (FDG) uptake.
 17. Theapparatus of claim 12, wherein the medical image data is magneticresonance image (MRI) data, positron emission tomography (PET) imagedata or single photon emission computed tomography (SPECT) image data.18. The apparatus of claim 12, wherein the subvolume analysis engine isconfigured to apply a probability density function for each of thecandidate parameters for each image segment.
 19. The apparatus of claim18, the subvolume analysis engine is configured to further determine theprobability density function for each of the candidate parameters for animage segment collected prior to treatment of the target tissue and foran image segment collected after starting treatment of the targettissue.
 20. The apparatus of claim 18, wherein the analysis comprisesmodeling the probability density functions using a fuzzy-c-means (FCM)clustering analysis, Gaussian mixture model and/or other discriminationanalyses.